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[SPARK-8517][ML][DOC] Reorganizes the spark.ml user guide

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This PR moves pieces of the spark.ml user guide to reflect suggestions in SPARK-8517. It does not introduce new content, as requested.

<img width="192" alt="screen shot 2015-12-08 at 11 36 00 am" src="https://cloud.githubusercontent.com/assets/7594753/11666166/e82b84f2-9d9f-11e5-8904-e215424d8444.png">

Author: Timothy Hunter <timhunter@databricks.com>

Closes #10207 from thunterdb/spark-8517.
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Timothy Hunter 2015-12-08 18:40:21 -08:00 committed by Joseph K. Bradley
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- text: Feature extraction, transformation, and selection
- text: "Overview: estimators, transformers and pipelines"
url: ml-intro.html
- text: Extracting, transforming and selecting features
url: ml-features.html
- text: Decision trees for classification and regression
url: ml-decision-tree.html
- text: Ensembles
url: ml-ensembles.html
- text: Linear methods with elastic-net regularization
url: ml-linear-methods.html
- text: Multilayer perceptron classifier
url: ml-ann.html
- text: Classification and Regression
url: ml-classification-regression.html
- text: Clustering
url: ml-clustering.html
- text: Advanced topics
url: ml-advanced.html

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---
layout: global
title: Advanced topics - spark.ml
displayTitle: Advanced topics
---
# Optimization of linear methods
The optimization algorithm underlying the implementation is called
[Orthant-Wise Limited-memory
QuasiNewton](http://research-srv.microsoft.com/en-us/um/people/jfgao/paper/icml07scalable.pdf)
(OWL-QN). It is an extension of L-BFGS that can effectively handle L1
regularization and elastic net.

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---
layout: global
title: Multilayer perceptron classifier - ML
displayTitle: <a href="ml-guide.html">ML</a> - Multilayer perceptron classifier
---
`\[
\newcommand{\R}{\mathbb{R}}
\newcommand{\E}{\mathbb{E}}
\newcommand{\x}{\mathbf{x}}
\newcommand{\y}{\mathbf{y}}
\newcommand{\wv}{\mathbf{w}}
\newcommand{\av}{\mathbf{\alpha}}
\newcommand{\bv}{\mathbf{b}}
\newcommand{\N}{\mathbb{N}}
\newcommand{\id}{\mathbf{I}}
\newcommand{\ind}{\mathbf{1}}
\newcommand{\0}{\mathbf{0}}
\newcommand{\unit}{\mathbf{e}}
\newcommand{\one}{\mathbf{1}}
\newcommand{\zero}{\mathbf{0}}
\]`
Multilayer perceptron classifier (MLPC) is a classifier based on the [feedforward artificial neural network](https://en.wikipedia.org/wiki/Feedforward_neural_network).
MLPC consists of multiple layers of nodes.
Each layer is fully connected to the next layer in the network. Nodes in the input layer represent the input data. All other nodes maps inputs to the outputs
by performing linear combination of the inputs with the node's weights `$\wv$` and bias `$\bv$` and applying an activation function.
It can be written in matrix form for MLPC with `$K+1$` layers as follows:
`\[
\mathrm{y}(\x) = \mathrm{f_K}(...\mathrm{f_2}(\wv_2^T\mathrm{f_1}(\wv_1^T \x+b_1)+b_2)...+b_K)
\]`
Nodes in intermediate layers use sigmoid (logistic) function:
`\[
\mathrm{f}(z_i) = \frac{1}{1 + e^{-z_i}}
\]`
Nodes in the output layer use softmax function:
`\[
\mathrm{f}(z_i) = \frac{e^{z_i}}{\sum_{k=1}^N e^{z_k}}
\]`
The number of nodes `$N$` in the output layer corresponds to the number of classes.
MLPC employes backpropagation for learning the model. We use logistic loss function for optimization and L-BFGS as optimization routine.
**Examples**
<div class="codetabs">
<div data-lang="scala" markdown="1">
{% include_example scala/org/apache/spark/examples/ml/MultilayerPerceptronClassifierExample.scala %}
</div>
<div data-lang="java" markdown="1">
{% include_example java/org/apache/spark/examples/ml/JavaMultilayerPerceptronClassifierExample.java %}
</div>
<div data-lang="python" markdown="1">
{% include_example python/ml/multilayer_perceptron_classification.py %}
</div>
</div>

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---
layout: global
title: Classification and regression - spark.ml
displayTitle: Classification and regression in spark.ml
---
`\[
\newcommand{\R}{\mathbb{R}}
\newcommand{\E}{\mathbb{E}}
\newcommand{\x}{\mathbf{x}}
\newcommand{\y}{\mathbf{y}}
\newcommand{\wv}{\mathbf{w}}
\newcommand{\av}{\mathbf{\alpha}}
\newcommand{\bv}{\mathbf{b}}
\newcommand{\N}{\mathbb{N}}
\newcommand{\id}{\mathbf{I}}
\newcommand{\ind}{\mathbf{1}}
\newcommand{\0}{\mathbf{0}}
\newcommand{\unit}{\mathbf{e}}
\newcommand{\one}{\mathbf{1}}
\newcommand{\zero}{\mathbf{0}}
\]`
**Table of Contents**
* This will become a table of contents (this text will be scraped).
{:toc}
In MLlib, we implement popular linear methods such as logistic
regression and linear least squares with $L_1$ or $L_2$ regularization.
Refer to [the linear methods in mllib](mllib-linear-methods.html) for
details. In `spark.ml`, we also include Pipelines API for [Elastic
net](http://en.wikipedia.org/wiki/Elastic_net_regularization), a hybrid
of $L_1$ and $L_2$ regularization proposed in [Zou et al, Regularization
and variable selection via the elastic
net](http://users.stat.umn.edu/~zouxx019/Papers/elasticnet.pdf).
Mathematically, it is defined as a convex combination of the $L_1$ and
the $L_2$ regularization terms:
`\[
\alpha \left( \lambda \|\wv\|_1 \right) + (1-\alpha) \left( \frac{\lambda}{2}\|\wv\|_2^2 \right) , \alpha \in [0, 1], \lambda \geq 0
\]`
By setting $\alpha$ properly, elastic net contains both $L_1$ and $L_2$
regularization as special cases. For example, if a [linear
regression](https://en.wikipedia.org/wiki/Linear_regression) model is
trained with the elastic net parameter $\alpha$ set to $1$, it is
equivalent to a
[Lasso](http://en.wikipedia.org/wiki/Least_squares#Lasso_method) model.
On the other hand, if $\alpha$ is set to $0$, the trained model reduces
to a [ridge
regression](http://en.wikipedia.org/wiki/Tikhonov_regularization) model.
We implement Pipelines API for both linear regression and logistic
regression with elastic net regularization.
# Classification
## Logistic regression
Logistic regression is a popular method to predict a binary response. It is a special case of [Generalized Linear models](https://en.wikipedia.org/wiki/Generalized_linear_model) that predicts the probability of the outcome.
For more background and more details about the implementation, refer to the documentation of the [logistic regression in `spark.mllib`](mllib-linear-methods.html#logistic-regression).
> The current implementation of logistic regression in `spark.ml` only supports binary classes. Support for multiclass regression will be added in the future.
**Example**
The following example shows how to train a logistic regression model
with elastic net regularization. `elasticNetParam` corresponds to
$\alpha$ and `regParam` corresponds to $\lambda$.
<div class="codetabs">
<div data-lang="scala" markdown="1">
{% include_example scala/org/apache/spark/examples/ml/LogisticRegressionWithElasticNetExample.scala %}
</div>
<div data-lang="java" markdown="1">
{% include_example java/org/apache/spark/examples/ml/JavaLogisticRegressionWithElasticNetExample.java %}
</div>
<div data-lang="python" markdown="1">
{% include_example python/ml/logistic_regression_with_elastic_net.py %}
</div>
</div>
The `spark.ml` implementation of logistic regression also supports
extracting a summary of the model over the training set. Note that the
predictions and metrics which are stored as `Dataframe` in
`BinaryLogisticRegressionSummary` are annotated `@transient` and hence
only available on the driver.
<div class="codetabs">
<div data-lang="scala" markdown="1">
[`LogisticRegressionTrainingSummary`](api/scala/index.html#org.apache.spark.ml.classification.LogisticRegressionTrainingSummary)
provides a summary for a
[`LogisticRegressionModel`](api/scala/index.html#org.apache.spark.ml.classification.LogisticRegressionModel).
Currently, only binary classification is supported and the
summary must be explicitly cast to
[`BinaryLogisticRegressionTrainingSummary`](api/scala/index.html#org.apache.spark.ml.classification.BinaryLogisticRegressionTrainingSummary).
This will likely change when multiclass classification is supported.
Continuing the earlier example:
{% include_example scala/org/apache/spark/examples/ml/LogisticRegressionSummaryExample.scala %}
</div>
<div data-lang="java" markdown="1">
[`LogisticRegressionTrainingSummary`](api/java/org/apache/spark/ml/classification/LogisticRegressionTrainingSummary.html)
provides a summary for a
[`LogisticRegressionModel`](api/java/org/apache/spark/ml/classification/LogisticRegressionModel.html).
Currently, only binary classification is supported and the
summary must be explicitly cast to
[`BinaryLogisticRegressionTrainingSummary`](api/java/org/apache/spark/ml/classification/BinaryLogisticRegressionTrainingSummary.html).
This will likely change when multiclass classification is supported.
Continuing the earlier example:
{% include_example java/org/apache/spark/examples/ml/JavaLogisticRegressionSummaryExample.java %}
</div>
<!--- TODO: Add python model summaries once implemented -->
<div data-lang="python" markdown="1">
Logistic regression model summary is not yet supported in Python.
</div>
</div>
## Decision tree classifier
Decision trees are a popular family of classification and regression methods.
More information about the `spark.ml` implementation can be found further in the [section on decision trees](#decision-trees).
**Example**
The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set.
We use two feature transformers to prepare the data; these help index categories for the label and categorical features, adding metadata to the `DataFrame` which the Decision Tree algorithm can recognize.
<div class="codetabs">
<div data-lang="scala" markdown="1">
More details on parameters can be found in the [Scala API documentation](api/scala/index.html#org.apache.spark.ml.classification.DecisionTreeClassifier).
{% include_example scala/org/apache/spark/examples/ml/DecisionTreeClassificationExample.scala %}
</div>
<div data-lang="java" markdown="1">
More details on parameters can be found in the [Java API documentation](api/java/org/apache/spark/ml/classification/DecisionTreeClassifier.html).
{% include_example java/org/apache/spark/examples/ml/JavaDecisionTreeClassificationExample.java %}
</div>
<div data-lang="python" markdown="1">
More details on parameters can be found in the [Python API documentation](api/python/pyspark.ml.html#pyspark.ml.classification.DecisionTreeClassifier).
{% include_example python/ml/decision_tree_classification_example.py %}
</div>
</div>
## Random forest classifier
Random forests are a popular family of classification and regression methods.
More information about the `spark.ml` implementation can be found further in the [section on random forests](#random-forests).
**Example**
The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set.
We use two feature transformers to prepare the data; these help index categories for the label and categorical features, adding metadata to the `DataFrame` which the tree-based algorithms can recognize.
<div class="codetabs">
<div data-lang="scala" markdown="1">
Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.classification.RandomForestClassifier) for more details.
{% include_example scala/org/apache/spark/examples/ml/RandomForestClassifierExample.scala %}
</div>
<div data-lang="java" markdown="1">
Refer to the [Java API docs](api/java/org/apache/spark/ml/classification/RandomForestClassifier.html) for more details.
{% include_example java/org/apache/spark/examples/ml/JavaRandomForestClassifierExample.java %}
</div>
<div data-lang="python" markdown="1">
Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.classification.RandomForestClassifier) for more details.
{% include_example python/ml/random_forest_classifier_example.py %}
</div>
</div>
## Gradient-boosted tree classifier
Gradient-boosted trees (GBTs) are a popular classification and regression method using ensembles of decision trees.
More information about the `spark.ml` implementation can be found further in the [section on GBTs](#gradient-boosted-trees-gbts).
**Example**
The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set.
We use two feature transformers to prepare the data; these help index categories for the label and categorical features, adding metadata to the `DataFrame` which the tree-based algorithms can recognize.
<div class="codetabs">
<div data-lang="scala" markdown="1">
Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.classification.GBTClassifier) for more details.
{% include_example scala/org/apache/spark/examples/ml/GradientBoostedTreeClassifierExample.scala %}
</div>
<div data-lang="java" markdown="1">
Refer to the [Java API docs](api/java/org/apache/spark/ml/classification/GBTClassifier.html) for more details.
{% include_example java/org/apache/spark/examples/ml/JavaGradientBoostedTreeClassifierExample.java %}
</div>
<div data-lang="python" markdown="1">
Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.classification.GBTClassifier) for more details.
{% include_example python/ml/gradient_boosted_tree_classifier_example.py %}
</div>
</div>
## Multilayer perceptron classifier
Multilayer perceptron classifier (MLPC) is a classifier based on the [feedforward artificial neural network](https://en.wikipedia.org/wiki/Feedforward_neural_network).
MLPC consists of multiple layers of nodes.
Each layer is fully connected to the next layer in the network. Nodes in the input layer represent the input data. All other nodes maps inputs to the outputs
by performing linear combination of the inputs with the node's weights `$\wv$` and bias `$\bv$` and applying an activation function.
It can be written in matrix form for MLPC with `$K+1$` layers as follows:
`\[
\mathrm{y}(\x) = \mathrm{f_K}(...\mathrm{f_2}(\wv_2^T\mathrm{f_1}(\wv_1^T \x+b_1)+b_2)...+b_K)
\]`
Nodes in intermediate layers use sigmoid (logistic) function:
`\[
\mathrm{f}(z_i) = \frac{1}{1 + e^{-z_i}}
\]`
Nodes in the output layer use softmax function:
`\[
\mathrm{f}(z_i) = \frac{e^{z_i}}{\sum_{k=1}^N e^{z_k}}
\]`
The number of nodes `$N$` in the output layer corresponds to the number of classes.
MLPC employes backpropagation for learning the model. We use logistic loss function for optimization and L-BFGS as optimization routine.
**Example**
<div class="codetabs">
<div data-lang="scala" markdown="1">
{% include_example scala/org/apache/spark/examples/ml/MultilayerPerceptronClassifierExample.scala %}
</div>
<div data-lang="java" markdown="1">
{% include_example java/org/apache/spark/examples/ml/JavaMultilayerPerceptronClassifierExample.java %}
</div>
<div data-lang="python" markdown="1">
{% include_example python/ml/multilayer_perceptron_classification.py %}
</div>
</div>
## One-vs-Rest classifier (a.k.a. One-vs-All)
[OneVsRest](http://en.wikipedia.org/wiki/Multiclass_classification#One-vs.-rest) is an example of a machine learning reduction for performing multiclass classification given a base classifier that can perform binary classification efficiently. It is also known as "One-vs-All."
`OneVsRest` is implemented as an `Estimator`. For the base classifier it takes instances of `Classifier` and creates a binary classification problem for each of the k classes. The classifier for class i is trained to predict whether the label is i or not, distinguishing class i from all other classes.
Predictions are done by evaluating each binary classifier and the index of the most confident classifier is output as label.
**Example**
The example below demonstrates how to load the
[Iris dataset](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/iris.scale), parse it as a DataFrame and perform multiclass classification using `OneVsRest`. The test error is calculated to measure the algorithm accuracy.
<div class="codetabs">
<div data-lang="scala" markdown="1">
Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.classifier.OneVsRest) for more details.
{% include_example scala/org/apache/spark/examples/ml/OneVsRestExample.scala %}
</div>
<div data-lang="java" markdown="1">
Refer to the [Java API docs](api/java/org/apache/spark/ml/classification/OneVsRest.html) for more details.
{% include_example java/org/apache/spark/examples/ml/JavaOneVsRestExample.java %}
</div>
</div>
# Regression
## Linear regression
The interface for working with linear regression models and model
summaries is similar to the logistic regression case.
**Example**
The following
example demonstrates training an elastic net regularized linear
regression model and extracting model summary statistics.
<div class="codetabs">
<div data-lang="scala" markdown="1">
{% include_example scala/org/apache/spark/examples/ml/LinearRegressionWithElasticNetExample.scala %}
</div>
<div data-lang="java" markdown="1">
{% include_example java/org/apache/spark/examples/ml/JavaLinearRegressionWithElasticNetExample.java %}
</div>
<div data-lang="python" markdown="1">
<!--- TODO: Add python model summaries once implemented -->
{% include_example python/ml/linear_regression_with_elastic_net.py %}
</div>
</div>
## Decision tree regression
Decision trees are a popular family of classification and regression methods.
More information about the `spark.ml` implementation can be found further in the [section on decision trees](#decision-trees).
**Example**
The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set.
We use a feature transformer to index categorical features, adding metadata to the `DataFrame` which the Decision Tree algorithm can recognize.
<div class="codetabs">
<div data-lang="scala" markdown="1">
More details on parameters can be found in the [Scala API documentation](api/scala/index.html#org.apache.spark.ml.regression.DecisionTreeRegressor).
{% include_example scala/org/apache/spark/examples/ml/DecisionTreeRegressionExample.scala %}
</div>
<div data-lang="java" markdown="1">
More details on parameters can be found in the [Java API documentation](api/java/org/apache/spark/ml/regression/DecisionTreeRegressor.html).
{% include_example java/org/apache/spark/examples/ml/JavaDecisionTreeRegressionExample.java %}
</div>
<div data-lang="python" markdown="1">
More details on parameters can be found in the [Python API documentation](api/python/pyspark.ml.html#pyspark.ml.regression.DecisionTreeRegressor).
{% include_example python/ml/decision_tree_regression_example.py %}
</div>
</div>
## Random forest regression
Random forests are a popular family of classification and regression methods.
More information about the `spark.ml` implementation can be found further in the [section on random forests](#random-forests).
**Example**
The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set.
We use a feature transformer to index categorical features, adding metadata to the `DataFrame` which the tree-based algorithms can recognize.
<div class="codetabs">
<div data-lang="scala" markdown="1">
Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.regression.RandomForestRegressor) for more details.
{% include_example scala/org/apache/spark/examples/ml/RandomForestRegressorExample.scala %}
</div>
<div data-lang="java" markdown="1">
Refer to the [Java API docs](api/java/org/apache/spark/ml/regression/RandomForestRegressor.html) for more details.
{% include_example java/org/apache/spark/examples/ml/JavaRandomForestRegressorExample.java %}
</div>
<div data-lang="python" markdown="1">
Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.regression.RandomForestRegressor) for more details.
{% include_example python/ml/random_forest_regressor_example.py %}
</div>
</div>
## Gradient-boosted tree regression
Gradient-boosted trees (GBTs) are a popular regression method using ensembles of decision trees.
More information about the `spark.ml` implementation can be found further in the [section on GBTs](#gradient-boosted-trees-gbts).
**Example**
Note: For this example dataset, `GBTRegressor` actually only needs 1 iteration, but that will not
be true in general.
<div class="codetabs">
<div data-lang="scala" markdown="1">
Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.regression.GBTRegressor) for more details.
{% include_example scala/org/apache/spark/examples/ml/GradientBoostedTreeRegressorExample.scala %}
</div>
<div data-lang="java" markdown="1">
Refer to the [Java API docs](api/java/org/apache/spark/ml/regression/GBTRegressor.html) for more details.
{% include_example java/org/apache/spark/examples/ml/JavaGradientBoostedTreeRegressorExample.java %}
</div>
<div data-lang="python" markdown="1">
Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.regression.GBTRegressor) for more details.
{% include_example python/ml/gradient_boosted_tree_regressor_example.py %}
</div>
</div>
## Survival regression
In `spark.ml`, we implement the [Accelerated failure time (AFT)](https://en.wikipedia.org/wiki/Accelerated_failure_time_model)
model which is a parametric survival regression model for censored data.
It describes a model for the log of survival time, so it's often called
log-linear model for survival analysis. Different from
[Proportional hazards](https://en.wikipedia.org/wiki/Proportional_hazards_model) model
designed for the same purpose, the AFT model is more easily to parallelize
because each instance contribute to the objective function independently.
Given the values of the covariates $x^{'}$, for random lifetime $t_{i}$ of
subjects i = 1, ..., n, with possible right-censoring,
the likelihood function under the AFT model is given as:
`\[
L(\beta,\sigma)=\prod_{i=1}^n[\frac{1}{\sigma}f_{0}(\frac{\log{t_{i}}-x^{'}\beta}{\sigma})]^{\delta_{i}}S_{0}(\frac{\log{t_{i}}-x^{'}\beta}{\sigma})^{1-\delta_{i}}
\]`
Where $\delta_{i}$ is the indicator of the event has occurred i.e. uncensored or not.
Using $\epsilon_{i}=\frac{\log{t_{i}}-x^{'}\beta}{\sigma}$, the log-likelihood function
assumes the form:
`\[
\iota(\beta,\sigma)=\sum_{i=1}^{n}[-\delta_{i}\log\sigma+\delta_{i}\log{f_{0}}(\epsilon_{i})+(1-\delta_{i})\log{S_{0}(\epsilon_{i})}]
\]`
Where $S_{0}(\epsilon_{i})$ is the baseline survivor function,
and $f_{0}(\epsilon_{i})$ is corresponding density function.
The most commonly used AFT model is based on the Weibull distribution of the survival time.
The Weibull distribution for lifetime corresponding to extreme value distribution for
log of the lifetime, and the $S_{0}(\epsilon)$ function is:
`\[
S_{0}(\epsilon_{i})=\exp(-e^{\epsilon_{i}})
\]`
the $f_{0}(\epsilon_{i})$ function is:
`\[
f_{0}(\epsilon_{i})=e^{\epsilon_{i}}\exp(-e^{\epsilon_{i}})
\]`
The log-likelihood function for AFT model with Weibull distribution of lifetime is:
`\[
\iota(\beta,\sigma)= -\sum_{i=1}^n[\delta_{i}\log\sigma-\delta_{i}\epsilon_{i}+e^{\epsilon_{i}}]
\]`
Due to minimizing the negative log-likelihood equivalent to maximum a posteriori probability,
the loss function we use to optimize is $-\iota(\beta,\sigma)$.
The gradient functions for $\beta$ and $\log\sigma$ respectively are:
`\[
\frac{\partial (-\iota)}{\partial \beta}=\sum_{1=1}^{n}[\delta_{i}-e^{\epsilon_{i}}]\frac{x_{i}}{\sigma}
\]`
`\[
\frac{\partial (-\iota)}{\partial (\log\sigma)}=\sum_{i=1}^{n}[\delta_{i}+(\delta_{i}-e^{\epsilon_{i}})\epsilon_{i}]
\]`
The AFT model can be formulated as a convex optimization problem,
i.e. the task of finding a minimizer of a convex function $-\iota(\beta,\sigma)$
that depends coefficients vector $\beta$ and the log of scale parameter $\log\sigma$.
The optimization algorithm underlying the implementation is L-BFGS.
The implementation matches the result from R's survival function
[survreg](https://stat.ethz.ch/R-manual/R-devel/library/survival/html/survreg.html)
**Example**
<div class="codetabs">
<div data-lang="scala" markdown="1">
{% include_example scala/org/apache/spark/examples/ml/AFTSurvivalRegressionExample.scala %}
</div>
<div data-lang="java" markdown="1">
{% include_example java/org/apache/spark/examples/ml/JavaAFTSurvivalRegressionExample.java %}
</div>
<div data-lang="python" markdown="1">
{% include_example python/ml/aft_survival_regression.py %}
</div>
</div>
# Decision trees
[Decision trees](http://en.wikipedia.org/wiki/Decision_tree_learning)
and their ensembles are popular methods for the machine learning tasks of
classification and regression. Decision trees are widely used since they are easy to interpret,
handle categorical features, extend to the multiclass classification setting, do not require
feature scaling, and are able to capture non-linearities and feature interactions. Tree ensemble
algorithms such as random forests and boosting are among the top performers for classification and
regression tasks.
MLlib supports decision trees for binary and multiclass classification and for regression,
using both continuous and categorical features. The implementation partitions data by rows,
allowing distributed training with millions or even billions of instances.
Users can find more information about the decision tree algorithm in the [MLlib Decision Tree guide](mllib-decision-tree.html).
The main differences between this API and the [original MLlib Decision Tree API](mllib-decision-tree.html) are:
* support for ML Pipelines
* separation of Decision Trees for classification vs. regression
* use of DataFrame metadata to distinguish continuous and categorical features
The Pipelines API for Decision Trees offers a bit more functionality than the original API. In particular, for classification, users can get the predicted probability of each class (a.k.a. class conditional probabilities).
Ensembles of trees (Random Forests and Gradient-Boosted Trees) are described below in the [Tree ensembles section](#tree-ensembles).
## Inputs and Outputs
We list the input and output (prediction) column types here.
All output columns are optional; to exclude an output column, set its corresponding Param to an empty string.
### Input Columns
<table class="table">
<thead>
<tr>
<th align="left">Param name</th>
<th align="left">Type(s)</th>
<th align="left">Default</th>
<th align="left">Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>labelCol</td>
<td>Double</td>
<td>"label"</td>
<td>Label to predict</td>
</tr>
<tr>
<td>featuresCol</td>
<td>Vector</td>
<td>"features"</td>
<td>Feature vector</td>
</tr>
</tbody>
</table>
### Output Columns
<table class="table">
<thead>
<tr>
<th align="left">Param name</th>
<th align="left">Type(s)</th>
<th align="left">Default</th>
<th align="left">Description</th>
<th align="left">Notes</th>
</tr>
</thead>
<tbody>
<tr>
<td>predictionCol</td>
<td>Double</td>
<td>"prediction"</td>
<td>Predicted label</td>
<td></td>
</tr>
<tr>
<td>rawPredictionCol</td>
<td>Vector</td>
<td>"rawPrediction"</td>
<td>Vector of length # classes, with the counts of training instance labels at the tree node which makes the prediction</td>
<td>Classification only</td>
</tr>
<tr>
<td>probabilityCol</td>
<td>Vector</td>
<td>"probability"</td>
<td>Vector of length # classes equal to rawPrediction normalized to a multinomial distribution</td>
<td>Classification only</td>
</tr>
</tbody>
</table>
# Tree Ensembles
The Pipelines API supports two major tree ensemble algorithms: [Random Forests](http://en.wikipedia.org/wiki/Random_forest) and [Gradient-Boosted Trees (GBTs)](http://en.wikipedia.org/wiki/Gradient_boosting).
Both use [MLlib decision trees](ml-decision-tree.html) as their base models.
Users can find more information about ensemble algorithms in the [MLlib Ensemble guide](mllib-ensembles.html). In this section, we demonstrate the Pipelines API for ensembles.
The main differences between this API and the [original MLlib ensembles API](mllib-ensembles.html) are:
* support for ML Pipelines
* separation of classification vs. regression
* use of DataFrame metadata to distinguish continuous and categorical features
* a bit more functionality for random forests: estimates of feature importance, as well as the predicted probability of each class (a.k.a. class conditional probabilities) for classification.
## Random Forests
[Random forests](http://en.wikipedia.org/wiki/Random_forest)
are ensembles of [decision trees](ml-decision-tree.html).
Random forests combine many decision trees in order to reduce the risk of overfitting.
MLlib supports random forests for binary and multiclass classification and for regression,
using both continuous and categorical features.
For more information on the algorithm itself, please see the [`spark.mllib` documentation on random forests](mllib-ensembles.html).
### Inputs and Outputs
We list the input and output (prediction) column types here.
All output columns are optional; to exclude an output column, set its corresponding Param to an empty string.
#### Input Columns
<table class="table">
<thead>
<tr>
<th align="left">Param name</th>
<th align="left">Type(s)</th>
<th align="left">Default</th>
<th align="left">Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>labelCol</td>
<td>Double</td>
<td>"label"</td>
<td>Label to predict</td>
</tr>
<tr>
<td>featuresCol</td>
<td>Vector</td>
<td>"features"</td>
<td>Feature vector</td>
</tr>
</tbody>
</table>
#### Output Columns (Predictions)
<table class="table">
<thead>
<tr>
<th align="left">Param name</th>
<th align="left">Type(s)</th>
<th align="left">Default</th>
<th align="left">Description</th>
<th align="left">Notes</th>
</tr>
</thead>
<tbody>
<tr>
<td>predictionCol</td>
<td>Double</td>
<td>"prediction"</td>
<td>Predicted label</td>
<td></td>
</tr>
<tr>
<td>rawPredictionCol</td>
<td>Vector</td>
<td>"rawPrediction"</td>
<td>Vector of length # classes, with the counts of training instance labels at the tree node which makes the prediction</td>
<td>Classification only</td>
</tr>
<tr>
<td>probabilityCol</td>
<td>Vector</td>
<td>"probability"</td>
<td>Vector of length # classes equal to rawPrediction normalized to a multinomial distribution</td>
<td>Classification only</td>
</tr>
</tbody>
</table>
## Gradient-Boosted Trees (GBTs)
[Gradient-Boosted Trees (GBTs)](http://en.wikipedia.org/wiki/Gradient_boosting)
are ensembles of [decision trees](ml-decision-tree.html).
GBTs iteratively train decision trees in order to minimize a loss function.
MLlib supports GBTs for binary classification and for regression,
using both continuous and categorical features.
For more information on the algorithm itself, please see the [`spark.mllib` documentation on GBTs](mllib-ensembles.html).
### Inputs and Outputs
We list the input and output (prediction) column types here.
All output columns are optional; to exclude an output column, set its corresponding Param to an empty string.
#### Input Columns
<table class="table">
<thead>
<tr>
<th align="left">Param name</th>
<th align="left">Type(s)</th>
<th align="left">Default</th>
<th align="left">Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>labelCol</td>
<td>Double</td>
<td>"label"</td>
<td>Label to predict</td>
</tr>
<tr>
<td>featuresCol</td>
<td>Vector</td>
<td>"features"</td>
<td>Feature vector</td>
</tr>
</tbody>
</table>
Note that `GBTClassifier` currently only supports binary labels.
#### Output Columns (Predictions)
<table class="table">
<thead>
<tr>
<th align="left">Param name</th>
<th align="left">Type(s)</th>
<th align="left">Default</th>
<th align="left">Description</th>
<th align="left">Notes</th>
</tr>
</thead>
<tbody>
<tr>
<td>predictionCol</td>
<td>Double</td>
<td>"prediction"</td>
<td>Predicted label</td>
<td></td>
</tr>
</tbody>
</table>
In the future, `GBTClassifier` will also output columns for `rawPrediction` and `probability`, just as `RandomForestClassifier` does.

5
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@ -6,6 +6,11 @@ displayTitle: <a href="ml-guide.html">ML</a> - Clustering
In this section, we introduce the pipeline API for [clustering in mllib](mllib-clustering.html).
**Table of Contents**
* This will become a table of contents (this text will be scraped).
{:toc}
## Latent Dirichlet allocation (LDA)
`LDA` is implemented as an `Estimator` that supports both `EMLDAOptimizer` and `OnlineLDAOptimizer`,

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---
layout: global
title: Feature Extraction, Transformation, and Selection - SparkML
displayTitle: <a href="ml-guide.html">ML</a> - Features
title: Extracting, transforming and selecting features
displayTitle: Extracting, transforming and selecting features
---
This section covers algorithms for working with features, roughly divided into these groups:

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---
layout: global
title: "Overview: estimators, transformers and pipelines - spark.ml"
displayTitle: "Overview: estimators, transformers and pipelines"
---
`\[
\newcommand{\R}{\mathbb{R}}
\newcommand{\E}{\mathbb{E}}
\newcommand{\x}{\mathbf{x}}
\newcommand{\y}{\mathbf{y}}
\newcommand{\wv}{\mathbf{w}}
\newcommand{\av}{\mathbf{\alpha}}
\newcommand{\bv}{\mathbf{b}}
\newcommand{\N}{\mathbb{N}}
\newcommand{\id}{\mathbf{I}}
\newcommand{\ind}{\mathbf{1}}
\newcommand{\0}{\mathbf{0}}
\newcommand{\unit}{\mathbf{e}}
\newcommand{\one}{\mathbf{1}}
\newcommand{\zero}{\mathbf{0}}
\]`
The `spark.ml` package aims to provide a uniform set of high-level APIs built on top of
[DataFrames](sql-programming-guide.html#dataframes) that help users create and tune practical
machine learning pipelines.
See the [algorithm guides](#algorithm-guides) section below for guides on sub-packages of
`spark.ml`, including feature transformers unique to the Pipelines API, ensembles, and more.
**Table of contents**
* This will become a table of contents (this text will be scraped).
{:toc}
# Main concepts in Pipelines
Spark ML standardizes APIs for machine learning algorithms to make it easier to combine multiple
algorithms into a single pipeline, or workflow.
This section covers the key concepts introduced by the Spark ML API, where the pipeline concept is
mostly inspired by the [scikit-learn](http://scikit-learn.org/) project.
* **[`DataFrame`](ml-guide.html#dataframe)**: Spark ML uses `DataFrame` from Spark SQL as an ML
dataset, which can hold a variety of data types.
E.g., a `DataFrame` could have different columns storing text, feature vectors, true labels, and predictions.
* **[`Transformer`](ml-guide.html#transformers)**: A `Transformer` is an algorithm which can transform one `DataFrame` into another `DataFrame`.
E.g., an ML model is a `Transformer` which transforms `DataFrame` with features into a `DataFrame` with predictions.
* **[`Estimator`](ml-guide.html#estimators)**: An `Estimator` is an algorithm which can be fit on a `DataFrame` to produce a `Transformer`.
E.g., a learning algorithm is an `Estimator` which trains on a `DataFrame` and produces a model.
* **[`Pipeline`](ml-guide.html#pipeline)**: A `Pipeline` chains multiple `Transformer`s and `Estimator`s together to specify an ML workflow.
* **[`Parameter`](ml-guide.html#parameters)**: All `Transformer`s and `Estimator`s now share a common API for specifying parameters.
## DataFrame
Machine learning can be applied to a wide variety of data types, such as vectors, text, images, and structured data.
Spark ML adopts the `DataFrame` from Spark SQL in order to support a variety of data types.
`DataFrame` supports many basic and structured types; see the [Spark SQL datatype reference](sql-programming-guide.html#spark-sql-datatype-reference) for a list of supported types.
In addition to the types listed in the Spark SQL guide, `DataFrame` can use ML [`Vector`](mllib-data-types.html#local-vector) types.
A `DataFrame` can be created either implicitly or explicitly from a regular `RDD`. See the code examples below and the [Spark SQL programming guide](sql-programming-guide.html) for examples.
Columns in a `DataFrame` are named. The code examples below use names such as "text," "features," and "label."
## Pipeline components
### Transformers
A `Transformer` is an abstraction that includes feature transformers and learned models.
Technically, a `Transformer` implements a method `transform()`, which converts one `DataFrame` into
another, generally by appending one or more columns.
For example:
* A feature transformer might take a `DataFrame`, read a column (e.g., text), map it into a new
column (e.g., feature vectors), and output a new `DataFrame` with the mapped column appended.
* A learning model might take a `DataFrame`, read the column containing feature vectors, predict the
label for each feature vector, and output a new `DataFrame` with predicted labels appended as a
column.
### Estimators
An `Estimator` abstracts the concept of a learning algorithm or any algorithm that fits or trains on
data.
Technically, an `Estimator` implements a method `fit()`, which accepts a `DataFrame` and produces a
`Model`, which is a `Transformer`.
For example, a learning algorithm such as `LogisticRegression` is an `Estimator`, and calling
`fit()` trains a `LogisticRegressionModel`, which is a `Model` and hence a `Transformer`.
### Properties of pipeline components
`Transformer.transform()`s and `Estimator.fit()`s are both stateless. In the future, stateful algorithms may be supported via alternative concepts.
Each instance of a `Transformer` or `Estimator` has a unique ID, which is useful in specifying parameters (discussed below).
## Pipeline
In machine learning, it is common to run a sequence of algorithms to process and learn from data.
E.g., a simple text document processing workflow might include several stages:
* Split each document's text into words.
* Convert each document's words into a numerical feature vector.
* Learn a prediction model using the feature vectors and labels.
Spark ML represents such a workflow as a `Pipeline`, which consists of a sequence of
`PipelineStage`s (`Transformer`s and `Estimator`s) to be run in a specific order.
We will use this simple workflow as a running example in this section.
### How it works
A `Pipeline` is specified as a sequence of stages, and each stage is either a `Transformer` or an `Estimator`.
These stages are run in order, and the input `DataFrame` is transformed as it passes through each stage.
For `Transformer` stages, the `transform()` method is called on the `DataFrame`.
For `Estimator` stages, the `fit()` method is called to produce a `Transformer` (which becomes part of the `PipelineModel`, or fitted `Pipeline`), and that `Transformer`'s `transform()` method is called on the `DataFrame`.
We illustrate this for the simple text document workflow. The figure below is for the *training time* usage of a `Pipeline`.
<p style="text-align: center;">
<img
src="img/ml-Pipeline.png"
title="Spark ML Pipeline Example"
alt="Spark ML Pipeline Example"
width="80%"
/>
</p>
Above, the top row represents a `Pipeline` with three stages.
The first two (`Tokenizer` and `HashingTF`) are `Transformer`s (blue), and the third (`LogisticRegression`) is an `Estimator` (red).
The bottom row represents data flowing through the pipeline, where cylinders indicate `DataFrame`s.
The `Pipeline.fit()` method is called on the original `DataFrame`, which has raw text documents and labels.
The `Tokenizer.transform()` method splits the raw text documents into words, adding a new column with words to the `DataFrame`.
The `HashingTF.transform()` method converts the words column into feature vectors, adding a new column with those vectors to the `DataFrame`.
Now, since `LogisticRegression` is an `Estimator`, the `Pipeline` first calls `LogisticRegression.fit()` to produce a `LogisticRegressionModel`.
If the `Pipeline` had more stages, it would call the `LogisticRegressionModel`'s `transform()`
method on the `DataFrame` before passing the `DataFrame` to the next stage.
A `Pipeline` is an `Estimator`.
Thus, after a `Pipeline`'s `fit()` method runs, it produces a `PipelineModel`, which is a
`Transformer`.
This `PipelineModel` is used at *test time*; the figure below illustrates this usage.
<p style="text-align: center;">
<img
src="img/ml-PipelineModel.png"
title="Spark ML PipelineModel Example"
alt="Spark ML PipelineModel Example"
width="80%"
/>
</p>
In the figure above, the `PipelineModel` has the same number of stages as the original `Pipeline`, but all `Estimator`s in the original `Pipeline` have become `Transformer`s.
When the `PipelineModel`'s `transform()` method is called on a test dataset, the data are passed
through the fitted pipeline in order.
Each stage's `transform()` method updates the dataset and passes it to the next stage.
`Pipeline`s and `PipelineModel`s help to ensure that training and test data go through identical feature processing steps.
### Details
*DAG `Pipeline`s*: A `Pipeline`'s stages are specified as an ordered array. The examples given here are all for linear `Pipeline`s, i.e., `Pipeline`s in which each stage uses data produced by the previous stage. It is possible to create non-linear `Pipeline`s as long as the data flow graph forms a Directed Acyclic Graph (DAG). This graph is currently specified implicitly based on the input and output column names of each stage (generally specified as parameters). If the `Pipeline` forms a DAG, then the stages must be specified in topological order.
*Runtime checking*: Since `Pipeline`s can operate on `DataFrame`s with varied types, they cannot use
compile-time type checking.
`Pipeline`s and `PipelineModel`s instead do runtime checking before actually running the `Pipeline`.
This type checking is done using the `DataFrame` *schema*, a description of the data types of columns in the `DataFrame`.
*Unique Pipeline stages*: A `Pipeline`'s stages should be unique instances. E.g., the same instance
`myHashingTF` should not be inserted into the `Pipeline` twice since `Pipeline` stages must have
unique IDs. However, different instances `myHashingTF1` and `myHashingTF2` (both of type `HashingTF`)
can be put into the same `Pipeline` since different instances will be created with different IDs.
## Parameters
Spark ML `Estimator`s and `Transformer`s use a uniform API for specifying parameters.
A `Param` is a named parameter with self-contained documentation.
A `ParamMap` is a set of (parameter, value) pairs.
There are two main ways to pass parameters to an algorithm:
1. Set parameters for an instance. E.g., if `lr` is an instance of `LogisticRegression`, one could
call `lr.setMaxIter(10)` to make `lr.fit()` use at most 10 iterations.
This API resembles the API used in `spark.mllib` package.
2. Pass a `ParamMap` to `fit()` or `transform()`. Any parameters in the `ParamMap` will override parameters previously specified via setter methods.
Parameters belong to specific instances of `Estimator`s and `Transformer`s.
For example, if we have two `LogisticRegression` instances `lr1` and `lr2`, then we can build a `ParamMap` with both `maxIter` parameters specified: `ParamMap(lr1.maxIter -> 10, lr2.maxIter -> 20)`.
This is useful if there are two algorithms with the `maxIter` parameter in a `Pipeline`.
# Code examples
This section gives code examples illustrating the functionality discussed above.
For more info, please refer to the API documentation
([Scala](api/scala/index.html#org.apache.spark.ml.package),
[Java](api/java/org/apache/spark/ml/package-summary.html),
and [Python](api/python/pyspark.ml.html)).
Some Spark ML algorithms are wrappers for `spark.mllib` algorithms, and the
[MLlib programming guide](mllib-guide.html) has details on specific algorithms.
## Example: Estimator, Transformer, and Param
This example covers the concepts of `Estimator`, `Transformer`, and `Param`.
<div class="codetabs">
<div data-lang="scala">
{% highlight scala %}
import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.param.ParamMap
import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.sql.Row
// Prepare training data from a list of (label, features) tuples.
val training = sqlContext.createDataFrame(Seq(
(1.0, Vectors.dense(0.0, 1.1, 0.1)),
(0.0, Vectors.dense(2.0, 1.0, -1.0)),
(0.0, Vectors.dense(2.0, 1.3, 1.0)),
(1.0, Vectors.dense(0.0, 1.2, -0.5))
)).toDF("label", "features")
// Create a LogisticRegression instance. This instance is an Estimator.
val lr = new LogisticRegression()
// Print out the parameters, documentation, and any default values.
println("LogisticRegression parameters:\n" + lr.explainParams() + "\n")
// We may set parameters using setter methods.
lr.setMaxIter(10)
.setRegParam(0.01)
// Learn a LogisticRegression model. This uses the parameters stored in lr.
val model1 = lr.fit(training)
// Since model1 is a Model (i.e., a Transformer produced by an Estimator),
// we can view the parameters it used during fit().
// This prints the parameter (name: value) pairs, where names are unique IDs for this
// LogisticRegression instance.
println("Model 1 was fit using parameters: " + model1.parent.extractParamMap)
// We may alternatively specify parameters using a ParamMap,
// which supports several methods for specifying parameters.
val paramMap = ParamMap(lr.maxIter -> 20)
.put(lr.maxIter, 30) // Specify 1 Param. This overwrites the original maxIter.
.put(lr.regParam -> 0.1, lr.threshold -> 0.55) // Specify multiple Params.
// One can also combine ParamMaps.
val paramMap2 = ParamMap(lr.probabilityCol -> "myProbability") // Change output column name
val paramMapCombined = paramMap ++ paramMap2
// Now learn a new model using the paramMapCombined parameters.
// paramMapCombined overrides all parameters set earlier via lr.set* methods.
val model2 = lr.fit(training, paramMapCombined)
println("Model 2 was fit using parameters: " + model2.parent.extractParamMap)
// Prepare test data.
val test = sqlContext.createDataFrame(Seq(
(1.0, Vectors.dense(-1.0, 1.5, 1.3)),
(0.0, Vectors.dense(3.0, 2.0, -0.1)),
(1.0, Vectors.dense(0.0, 2.2, -1.5))
)).toDF("label", "features")
// Make predictions on test data using the Transformer.transform() method.
// LogisticRegression.transform will only use the 'features' column.
// Note that model2.transform() outputs a 'myProbability' column instead of the usual
// 'probability' column since we renamed the lr.probabilityCol parameter previously.
model2.transform(test)
.select("features", "label", "myProbability", "prediction")
.collect()
.foreach { case Row(features: Vector, label: Double, prob: Vector, prediction: Double) =>
println(s"($features, $label) -> prob=$prob, prediction=$prediction")
}
{% endhighlight %}
</div>
<div data-lang="java">
{% highlight java %}
import java.util.Arrays;
import java.util.List;
import org.apache.spark.ml.classification.LogisticRegressionModel;
import org.apache.spark.ml.param.ParamMap;
import org.apache.spark.ml.classification.LogisticRegression;
import org.apache.spark.mllib.linalg.Vectors;
import org.apache.spark.mllib.regression.LabeledPoint;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.Row;
// Prepare training data.
// We use LabeledPoint, which is a JavaBean. Spark SQL can convert RDDs of JavaBeans
// into DataFrames, where it uses the bean metadata to infer the schema.
DataFrame training = sqlContext.createDataFrame(Arrays.asList(
new LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)),
new LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)),
new LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)),
new LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5))
), LabeledPoint.class);
// Create a LogisticRegression instance. This instance is an Estimator.
LogisticRegression lr = new LogisticRegression();
// Print out the parameters, documentation, and any default values.
System.out.println("LogisticRegression parameters:\n" + lr.explainParams() + "\n");
// We may set parameters using setter methods.
lr.setMaxIter(10)
.setRegParam(0.01);
// Learn a LogisticRegression model. This uses the parameters stored in lr.
LogisticRegressionModel model1 = lr.fit(training);
// Since model1 is a Model (i.e., a Transformer produced by an Estimator),
// we can view the parameters it used during fit().
// This prints the parameter (name: value) pairs, where names are unique IDs for this
// LogisticRegression instance.
System.out.println("Model 1 was fit using parameters: " + model1.parent().extractParamMap());
// We may alternatively specify parameters using a ParamMap.
ParamMap paramMap = new ParamMap()
.put(lr.maxIter().w(20)) // Specify 1 Param.
.put(lr.maxIter(), 30) // This overwrites the original maxIter.
.put(lr.regParam().w(0.1), lr.threshold().w(0.55)); // Specify multiple Params.
// One can also combine ParamMaps.
ParamMap paramMap2 = new ParamMap()
.put(lr.probabilityCol().w("myProbability")); // Change output column name
ParamMap paramMapCombined = paramMap.$plus$plus(paramMap2);
// Now learn a new model using the paramMapCombined parameters.
// paramMapCombined overrides all parameters set earlier via lr.set* methods.
LogisticRegressionModel model2 = lr.fit(training, paramMapCombined);
System.out.println("Model 2 was fit using parameters: " + model2.parent().extractParamMap());
// Prepare test documents.
DataFrame test = sqlContext.createDataFrame(Arrays.asList(
new LabeledPoint(1.0, Vectors.dense(-1.0, 1.5, 1.3)),
new LabeledPoint(0.0, Vectors.dense(3.0, 2.0, -0.1)),
new LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5))
), LabeledPoint.class);
// Make predictions on test documents using the Transformer.transform() method.
// LogisticRegression.transform will only use the 'features' column.
// Note that model2.transform() outputs a 'myProbability' column instead of the usual
// 'probability' column since we renamed the lr.probabilityCol parameter previously.
DataFrame results = model2.transform(test);
for (Row r: results.select("features", "label", "myProbability", "prediction").collect()) {
System.out.println("(" + r.get(0) + ", " + r.get(1) + ") -> prob=" + r.get(2)
+ ", prediction=" + r.get(3));
}
{% endhighlight %}
</div>
<div data-lang="python">
{% highlight python %}
from pyspark.mllib.linalg import Vectors
from pyspark.ml.classification import LogisticRegression
from pyspark.ml.param import Param, Params
# Prepare training data from a list of (label, features) tuples.
training = sqlContext.createDataFrame([
(1.0, Vectors.dense([0.0, 1.1, 0.1])),
(0.0, Vectors.dense([2.0, 1.0, -1.0])),
(0.0, Vectors.dense([2.0, 1.3, 1.0])),
(1.0, Vectors.dense([0.0, 1.2, -0.5]))], ["label", "features"])
# Create a LogisticRegression instance. This instance is an Estimator.
lr = LogisticRegression(maxIter=10, regParam=0.01)
# Print out the parameters, documentation, and any default values.
print "LogisticRegression parameters:\n" + lr.explainParams() + "\n"
# Learn a LogisticRegression model. This uses the parameters stored in lr.
model1 = lr.fit(training)
# Since model1 is a Model (i.e., a transformer produced by an Estimator),
# we can view the parameters it used during fit().
# This prints the parameter (name: value) pairs, where names are unique IDs for this
# LogisticRegression instance.
print "Model 1 was fit using parameters: "
print model1.extractParamMap()
# We may alternatively specify parameters using a Python dictionary as a paramMap
paramMap = {lr.maxIter: 20}
paramMap[lr.maxIter] = 30 # Specify 1 Param, overwriting the original maxIter.
paramMap.update({lr.regParam: 0.1, lr.threshold: 0.55}) # Specify multiple Params.
# You can combine paramMaps, which are python dictionaries.
paramMap2 = {lr.probabilityCol: "myProbability"} # Change output column name
paramMapCombined = paramMap.copy()
paramMapCombined.update(paramMap2)
# Now learn a new model using the paramMapCombined parameters.
# paramMapCombined overrides all parameters set earlier via lr.set* methods.
model2 = lr.fit(training, paramMapCombined)
print "Model 2 was fit using parameters: "
print model2.extractParamMap()
# Prepare test data
test = sqlContext.createDataFrame([
(1.0, Vectors.dense([-1.0, 1.5, 1.3])),
(0.0, Vectors.dense([3.0, 2.0, -0.1])),
(1.0, Vectors.dense([0.0, 2.2, -1.5]))], ["label", "features"])
# Make predictions on test data using the Transformer.transform() method.
# LogisticRegression.transform will only use the 'features' column.
# Note that model2.transform() outputs a "myProbability" column instead of the usual
# 'probability' column since we renamed the lr.probabilityCol parameter previously.
prediction = model2.transform(test)
selected = prediction.select("features", "label", "myProbability", "prediction")
for row in selected.collect():
print row
{% endhighlight %}
</div>
</div>
## Example: Pipeline
This example follows the simple text document `Pipeline` illustrated in the figures above.
<div class="codetabs">
<div data-lang="scala">
{% highlight scala %}
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.feature.{HashingTF, Tokenizer}
import org.apache.spark.mllib.linalg.Vector
import org.apache.spark.sql.Row
// Prepare training documents from a list of (id, text, label) tuples.
val training = sqlContext.createDataFrame(Seq(
(0L, "a b c d e spark", 1.0),
(1L, "b d", 0.0),
(2L, "spark f g h", 1.0),
(3L, "hadoop mapreduce", 0.0)
)).toDF("id", "text", "label")
// Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
val tokenizer = new Tokenizer()
.setInputCol("text")
.setOutputCol("words")
val hashingTF = new HashingTF()
.setNumFeatures(1000)
.setInputCol(tokenizer.getOutputCol)
.setOutputCol("features")
val lr = new LogisticRegression()
.setMaxIter(10)
.setRegParam(0.01)
val pipeline = new Pipeline()
.setStages(Array(tokenizer, hashingTF, lr))
// Fit the pipeline to training documents.
val model = pipeline.fit(training)
// Prepare test documents, which are unlabeled (id, text) tuples.
val test = sqlContext.createDataFrame(Seq(
(4L, "spark i j k"),
(5L, "l m n"),
(6L, "mapreduce spark"),
(7L, "apache hadoop")
)).toDF("id", "text")
// Make predictions on test documents.
model.transform(test)
.select("id", "text", "probability", "prediction")
.collect()
.foreach { case Row(id: Long, text: String, prob: Vector, prediction: Double) =>
println(s"($id, $text) --> prob=$prob, prediction=$prediction")
}
{% endhighlight %}
</div>
<div data-lang="java">
{% highlight java %}
import java.util.Arrays;
import java.util.List;
import org.apache.spark.ml.Pipeline;
import org.apache.spark.ml.PipelineModel;
import org.apache.spark.ml.PipelineStage;
import org.apache.spark.ml.classification.LogisticRegression;
import org.apache.spark.ml.feature.HashingTF;
import org.apache.spark.ml.feature.Tokenizer;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.Row;
// Labeled and unlabeled instance types.
// Spark SQL can infer schema from Java Beans.
public class Document implements Serializable {
private long id;
private String text;
public Document(long id, String text) {
this.id = id;
this.text = text;
}
public long getId() { return this.id; }
public void setId(long id) { this.id = id; }
public String getText() { return this.text; }
public void setText(String text) { this.text = text; }
}
public class LabeledDocument extends Document implements Serializable {
private double label;
public LabeledDocument(long id, String text, double label) {
super(id, text);
this.label = label;
}
public double getLabel() { return this.label; }
public void setLabel(double label) { this.label = label; }
}
// Prepare training documents, which are labeled.
DataFrame training = sqlContext.createDataFrame(Arrays.asList(
new LabeledDocument(0L, "a b c d e spark", 1.0),
new LabeledDocument(1L, "b d", 0.0),
new LabeledDocument(2L, "spark f g h", 1.0),
new LabeledDocument(3L, "hadoop mapreduce", 0.0)
), LabeledDocument.class);
// Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
Tokenizer tokenizer = new Tokenizer()
.setInputCol("text")
.setOutputCol("words");
HashingTF hashingTF = new HashingTF()
.setNumFeatures(1000)
.setInputCol(tokenizer.getOutputCol())
.setOutputCol("features");
LogisticRegression lr = new LogisticRegression()
.setMaxIter(10)
.setRegParam(0.01);
Pipeline pipeline = new Pipeline()
.setStages(new PipelineStage[] {tokenizer, hashingTF, lr});
// Fit the pipeline to training documents.
PipelineModel model = pipeline.fit(training);
// Prepare test documents, which are unlabeled.
DataFrame test = sqlContext.createDataFrame(Arrays.asList(
new Document(4L, "spark i j k"),
new Document(5L, "l m n"),
new Document(6L, "mapreduce spark"),
new Document(7L, "apache hadoop")
), Document.class);
// Make predictions on test documents.
DataFrame predictions = model.transform(test);
for (Row r: predictions.select("id", "text", "probability", "prediction").collect()) {
System.out.println("(" + r.get(0) + ", " + r.get(1) + ") --> prob=" + r.get(2)
+ ", prediction=" + r.get(3));
}
{% endhighlight %}
</div>
<div data-lang="python">
{% highlight python %}
from pyspark.ml import Pipeline
from pyspark.ml.classification import LogisticRegression
from pyspark.ml.feature import HashingTF, Tokenizer
from pyspark.sql import Row
# Prepare training documents from a list of (id, text, label) tuples.
LabeledDocument = Row("id", "text", "label")
training = sqlContext.createDataFrame([
(0L, "a b c d e spark", 1.0),
(1L, "b d", 0.0),
(2L, "spark f g h", 1.0),
(3L, "hadoop mapreduce", 0.0)], ["id", "text", "label"])
# Configure an ML pipeline, which consists of tree stages: tokenizer, hashingTF, and lr.
tokenizer = Tokenizer(inputCol="text", outputCol="words")
hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features")
lr = LogisticRegression(maxIter=10, regParam=0.01)
pipeline = Pipeline(stages=[tokenizer, hashingTF, lr])
# Fit the pipeline to training documents.
model = pipeline.fit(training)
# Prepare test documents, which are unlabeled (id, text) tuples.
test = sqlContext.createDataFrame([
(4L, "spark i j k"),
(5L, "l m n"),
(6L, "mapreduce spark"),
(7L, "apache hadoop")], ["id", "text"])
# Make predictions on test documents and print columns of interest.
prediction = model.transform(test)
selected = prediction.select("id", "text", "prediction")
for row in selected.collect():
print(row)
{% endhighlight %}
</div>
</div>
## Example: model selection via cross-validation
An important task in ML is *model selection*, or using data to find the best model or parameters for a given task. This is also called *tuning*.
`Pipeline`s facilitate model selection by making it easy to tune an entire `Pipeline` at once, rather than tuning each element in the `Pipeline` separately.
Currently, `spark.ml` supports model selection using the [`CrossValidator`](api/scala/index.html#org.apache.spark.ml.tuning.CrossValidator) class, which takes an `Estimator`, a set of `ParamMap`s, and an [`Evaluator`](api/scala/index.html#org.apache.spark.ml.evaluation.Evaluator).
`CrossValidator` begins by splitting the dataset into a set of *folds* which are used as separate training and test datasets; e.g., with `$k=3$` folds, `CrossValidator` will generate 3 (training, test) dataset pairs, each of which uses 2/3 of the data for training and 1/3 for testing.
`CrossValidator` iterates through the set of `ParamMap`s. For each `ParamMap`, it trains the given `Estimator` and evaluates it using the given `Evaluator`.
The `Evaluator` can be a [`RegressionEvaluator`](api/scala/index.html#org.apache.spark.ml.evaluation.RegressionEvaluator)
for regression problems, a [`BinaryClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.evaluation.BinaryClassificationEvaluator)
for binary data, or a [`MultiClassClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.evaluation.MultiClassClassificationEvaluator)
for multiclass problems. The default metric used to choose the best `ParamMap` can be overriden by the `setMetric`
method in each of these evaluators.
The `ParamMap` which produces the best evaluation metric (averaged over the `$k$` folds) is selected as the best model.
`CrossValidator` finally fits the `Estimator` using the best `ParamMap` and the entire dataset.
The following example demonstrates using `CrossValidator` to select from a grid of parameters.
To help construct the parameter grid, we use the [`ParamGridBuilder`](api/scala/index.html#org.apache.spark.ml.tuning.ParamGridBuilder) utility.
Note that cross-validation over a grid of parameters is expensive.
E.g., in the example below, the parameter grid has 3 values for `hashingTF.numFeatures` and 2 values for `lr.regParam`, and `CrossValidator` uses 2 folds. This multiplies out to `$(3 \times 2) \times 2 = 12$` different models being trained.
In realistic settings, it can be common to try many more parameters and use more folds (`$k=3$` and `$k=10$` are common).
In other words, using `CrossValidator` can be very expensive.
However, it is also a well-established method for choosing parameters which is more statistically sound than heuristic hand-tuning.
<div class="codetabs">
<div data-lang="scala">
{% highlight scala %}
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
import org.apache.spark.ml.feature.{HashingTF, Tokenizer}
import org.apache.spark.ml.tuning.{ParamGridBuilder, CrossValidator}
import org.apache.spark.mllib.linalg.Vector
import org.apache.spark.sql.Row
// Prepare training data from a list of (id, text, label) tuples.
val training = sqlContext.createDataFrame(Seq(
(0L, "a b c d e spark", 1.0),
(1L, "b d", 0.0),
(2L, "spark f g h", 1.0),
(3L, "hadoop mapreduce", 0.0),
(4L, "b spark who", 1.0),
(5L, "g d a y", 0.0),
(6L, "spark fly", 1.0),
(7L, "was mapreduce", 0.0),
(8L, "e spark program", 1.0),
(9L, "a e c l", 0.0),
(10L, "spark compile", 1.0),
(11L, "hadoop software", 0.0)
)).toDF("id", "text", "label")
// Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
val tokenizer = new Tokenizer()
.setInputCol("text")
.setOutputCol("words")
val hashingTF = new HashingTF()
.setInputCol(tokenizer.getOutputCol)
.setOutputCol("features")
val lr = new LogisticRegression()
.setMaxIter(10)
val pipeline = new Pipeline()
.setStages(Array(tokenizer, hashingTF, lr))
// We use a ParamGridBuilder to construct a grid of parameters to search over.
// With 3 values for hashingTF.numFeatures and 2 values for lr.regParam,
// this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from.
val paramGrid = new ParamGridBuilder()
.addGrid(hashingTF.numFeatures, Array(10, 100, 1000))
.addGrid(lr.regParam, Array(0.1, 0.01))
.build()
// We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance.
// This will allow us to jointly choose parameters for all Pipeline stages.
// A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
// Note that the evaluator here is a BinaryClassificationEvaluator and its default metric
// is areaUnderROC.
val cv = new CrossValidator()
.setEstimator(pipeline)
.setEvaluator(new BinaryClassificationEvaluator)
.setEstimatorParamMaps(paramGrid)
.setNumFolds(2) // Use 3+ in practice
// Run cross-validation, and choose the best set of parameters.
val cvModel = cv.fit(training)
// Prepare test documents, which are unlabeled (id, text) tuples.
val test = sqlContext.createDataFrame(Seq(
(4L, "spark i j k"),
(5L, "l m n"),
(6L, "mapreduce spark"),
(7L, "apache hadoop")
)).toDF("id", "text")
// Make predictions on test documents. cvModel uses the best model found (lrModel).
cvModel.transform(test)
.select("id", "text", "probability", "prediction")
.collect()
.foreach { case Row(id: Long, text: String, prob: Vector, prediction: Double) =>
println(s"($id, $text) --> prob=$prob, prediction=$prediction")
}
{% endhighlight %}
</div>
<div data-lang="java">
{% highlight java %}
import java.util.Arrays;
import java.util.List;
import org.apache.spark.ml.Pipeline;
import org.apache.spark.ml.PipelineStage;
import org.apache.spark.ml.classification.LogisticRegression;
import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator;
import org.apache.spark.ml.feature.HashingTF;
import org.apache.spark.ml.feature.Tokenizer;
import org.apache.spark.ml.param.ParamMap;
import org.apache.spark.ml.tuning.CrossValidator;
import org.apache.spark.ml.tuning.CrossValidatorModel;
import org.apache.spark.ml.tuning.ParamGridBuilder;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.Row;
// Labeled and unlabeled instance types.
// Spark SQL can infer schema from Java Beans.
public class Document implements Serializable {
private long id;
private String text;
public Document(long id, String text) {
this.id = id;
this.text = text;
}
public long getId() { return this.id; }
public void setId(long id) { this.id = id; }
public String getText() { return this.text; }
public void setText(String text) { this.text = text; }
}
public class LabeledDocument extends Document implements Serializable {
private double label;
public LabeledDocument(long id, String text, double label) {
super(id, text);
this.label = label;
}
public double getLabel() { return this.label; }
public void setLabel(double label) { this.label = label; }
}
// Prepare training documents, which are labeled.
DataFrame training = sqlContext.createDataFrame(Arrays.asList(
new LabeledDocument(0L, "a b c d e spark", 1.0),
new LabeledDocument(1L, "b d", 0.0),
new LabeledDocument(2L, "spark f g h", 1.0),
new LabeledDocument(3L, "hadoop mapreduce", 0.0),
new LabeledDocument(4L, "b spark who", 1.0),
new LabeledDocument(5L, "g d a y", 0.0),
new LabeledDocument(6L, "spark fly", 1.0),
new LabeledDocument(7L, "was mapreduce", 0.0),
new LabeledDocument(8L, "e spark program", 1.0),
new LabeledDocument(9L, "a e c l", 0.0),
new LabeledDocument(10L, "spark compile", 1.0),
new LabeledDocument(11L, "hadoop software", 0.0)
), LabeledDocument.class);
// Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
Tokenizer tokenizer = new Tokenizer()
.setInputCol("text")
.setOutputCol("words");
HashingTF hashingTF = new HashingTF()
.setNumFeatures(1000)
.setInputCol(tokenizer.getOutputCol())
.setOutputCol("features");
LogisticRegression lr = new LogisticRegression()
.setMaxIter(10)
.setRegParam(0.01);
Pipeline pipeline = new Pipeline()
.setStages(new PipelineStage[] {tokenizer, hashingTF, lr});
// We use a ParamGridBuilder to construct a grid of parameters to search over.
// With 3 values for hashingTF.numFeatures and 2 values for lr.regParam,
// this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from.
ParamMap[] paramGrid = new ParamGridBuilder()
.addGrid(hashingTF.numFeatures(), new int[]{10, 100, 1000})
.addGrid(lr.regParam(), new double[]{0.1, 0.01})
.build();
// We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance.
// This will allow us to jointly choose parameters for all Pipeline stages.
// A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
// Note that the evaluator here is a BinaryClassificationEvaluator and its default metric
// is areaUnderROC.
CrossValidator cv = new CrossValidator()
.setEstimator(pipeline)
.setEvaluator(new BinaryClassificationEvaluator())
.setEstimatorParamMaps(paramGrid)
.setNumFolds(2); // Use 3+ in practice
// Run cross-validation, and choose the best set of parameters.
CrossValidatorModel cvModel = cv.fit(training);
// Prepare test documents, which are unlabeled.
DataFrame test = sqlContext.createDataFrame(Arrays.asList(
new Document(4L, "spark i j k"),
new Document(5L, "l m n"),
new Document(6L, "mapreduce spark"),
new Document(7L, "apache hadoop")
), Document.class);
// Make predictions on test documents. cvModel uses the best model found (lrModel).
DataFrame predictions = cvModel.transform(test);
for (Row r: predictions.select("id", "text", "probability", "prediction").collect()) {
System.out.println("(" + r.get(0) + ", " + r.get(1) + ") --> prob=" + r.get(2)
+ ", prediction=" + r.get(3));
}
{% endhighlight %}
</div>
</div>
## Example: model selection via train validation split
In addition to `CrossValidator` Spark also offers `TrainValidationSplit` for hyper-parameter tuning.
`TrainValidationSplit` only evaluates each combination of parameters once as opposed to k times in
case of `CrossValidator`. It is therefore less expensive,
but will not produce as reliable results when the training dataset is not sufficiently large.
`TrainValidationSplit` takes an `Estimator`, a set of `ParamMap`s provided in the `estimatorParamMaps` parameter,
and an `Evaluator`.
It begins by splitting the dataset into two parts using `trainRatio` parameter
which are used as separate training and test datasets. For example with `$trainRatio=0.75$` (default),
`TrainValidationSplit` will generate a training and test dataset pair where 75% of the data is used for training and 25% for validation.
Similar to `CrossValidator`, `TrainValidationSplit` also iterates through the set of `ParamMap`s.
For each combination of parameters, it trains the given `Estimator` and evaluates it using the given `Evaluator`.
The `ParamMap` which produces the best evaluation metric is selected as the best option.
`TrainValidationSplit` finally fits the `Estimator` using the best `ParamMap` and the entire dataset.
<div class="codetabs">
<div data-lang="scala" markdown="1">
{% highlight scala %}
import org.apache.spark.ml.evaluation.RegressionEvaluator
import org.apache.spark.ml.regression.LinearRegression
import org.apache.spark.ml.tuning.{ParamGridBuilder, TrainValidationSplit}
// Prepare training and test data.
val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
val Array(training, test) = data.randomSplit(Array(0.9, 0.1), seed = 12345)
val lr = new LinearRegression()
// We use a ParamGridBuilder to construct a grid of parameters to search over.
// TrainValidationSplit will try all combinations of values and determine best model using
// the evaluator.
val paramGrid = new ParamGridBuilder()
.addGrid(lr.regParam, Array(0.1, 0.01))
.addGrid(lr.fitIntercept)
.addGrid(lr.elasticNetParam, Array(0.0, 0.5, 1.0))
.build()
// In this case the estimator is simply the linear regression.
// A TrainValidationSplit requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
val trainValidationSplit = new TrainValidationSplit()
.setEstimator(lr)
.setEvaluator(new RegressionEvaluator)
.setEstimatorParamMaps(paramGrid)
// 80% of the data will be used for training and the remaining 20% for validation.
.setTrainRatio(0.8)
// Run train validation split, and choose the best set of parameters.
val model = trainValidationSplit.fit(training)
// Make predictions on test data. model is the model with combination of parameters
// that performed best.
model.transform(test)
.select("features", "label", "prediction")
.show()
{% endhighlight %}
</div>
<div data-lang="java" markdown="1">
{% highlight java %}
import org.apache.spark.ml.evaluation.RegressionEvaluator;
import org.apache.spark.ml.param.ParamMap;
import org.apache.spark.ml.regression.LinearRegression;
import org.apache.spark.ml.tuning.*;
import org.apache.spark.sql.DataFrame;
DataFrame data = jsql.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt");
// Prepare training and test data.
DataFrame[] splits = data.randomSplit(new double[] {0.9, 0.1}, 12345);
DataFrame training = splits[0];
DataFrame test = splits[1];
LinearRegression lr = new LinearRegression();
// We use a ParamGridBuilder to construct a grid of parameters to search over.
// TrainValidationSplit will try all combinations of values and determine best model using
// the evaluator.
ParamMap[] paramGrid = new ParamGridBuilder()
.addGrid(lr.regParam(), new double[] {0.1, 0.01})
.addGrid(lr.fitIntercept())
.addGrid(lr.elasticNetParam(), new double[] {0.0, 0.5, 1.0})
.build();
// In this case the estimator is simply the linear regression.
// A TrainValidationSplit requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
TrainValidationSplit trainValidationSplit = new TrainValidationSplit()
.setEstimator(lr)
.setEvaluator(new RegressionEvaluator())
.setEstimatorParamMaps(paramGrid)
.setTrainRatio(0.8); // 80% for training and the remaining 20% for validation
// Run train validation split, and choose the best set of parameters.
TrainValidationSplitModel model = trainValidationSplit.fit(training);
// Make predictions on test data. model is the model with combination of parameters
// that performed best.
model.transform(test)
.select("features", "label", "prediction")
.show();
{% endhighlight %}
</div>
</div>

15
docs/mllib-guide.md
View file

@ -66,15 +66,14 @@ We list major functionality from both below, with links to detailed guides.
# spark.ml: high-level APIs for ML pipelines
**[spark.ml programming guide](ml-guide.html)** provides an overview of the Pipelines API and major
concepts. It also contains sections on using algorithms within the Pipelines API, for example:
* [Feature extraction, transformation, and selection](ml-features.html)
* [Overview: estimators, transformers and pipelines](ml-intro.html)
* [Extracting, transforming and selecting features](ml-features.html)
* [Classification and regression](ml-classification-regression.html)
* [Clustering](ml-clustering.html)
* [Decision trees for classification and regression](ml-decision-tree.html)
* [Ensembles](ml-ensembles.html)
* [Linear methods with elastic net regularization](ml-linear-methods.html)
* [Multilayer perceptron classifier](ml-ann.html)
* [Advanced topics](ml-advanced.html)
Some techniques are not available yet in spark.ml, most notably dimensionality reduction
Users can seemlessly combine the implementation of these techniques found in `spark.mllib` with the rest of the algorithms found in `spark.ml`.
# Dependencies