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tex formulas in the documentation using mathjax. and spliting the MLlib documentation by techniques see jira https://spark-project.atlassian.net/browse/MLLIB-19 and https://github.com/shivaram/spark/compare/mathjax Author: Martin Jaggi <m.jaggi@gmail.com> == Merge branch commits == commit 0364bfabbfc347f917216057a20c39b631842481 Author: Martin Jaggi <m.jaggi@gmail.com> Date: Fri Feb 7 03:19:38 2014 +0100 minor polishing, as suggested by @pwendell commit dcd2142c164b2f602bf472bb152ad55bae82d31a Author: Martin Jaggi <m.jaggi@gmail.com> Date: Thu Feb 6 18:04:26 2014 +0100 enabling inline latex formulas with $.$ same mathjax configuration as used in math.stackexchange.com sample usage in the linear algebra (SVD) documentation commit bbafafd2b497a5acaa03a140bb9de1fbb7d67ffa Author: Martin Jaggi <m.jaggi@gmail.com> Date: Thu Feb 6 17:31:29 2014 +0100 split MLlib documentation by techniques and linked from the main mllib-guide.md site commit d1c5212b93c67436543c2d8ddbbf610fdf0a26eb Author: Martin Jaggi <m.jaggi@gmail.com> Date: Thu Feb 6 16:59:43 2014 +0100 enable mathjax formula in the .md documentation files code by @shivaram commit d73948db0d9bc36296054e79fec5b1a657b4eab4 Author: Martin Jaggi <m.jaggi@gmail.com> Date: Thu Feb 6 16:57:23 2014 +0100 minor update on how to compile the documentation
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| layout | title |
|---|---|
| global | Machine Learning Library (MLlib) |
MLlib is a Spark implementation of some common machine learning (ML) functionality, as well associated tests and data generators. MLlib currently supports four common types of machine learning problem settings, namely, binary classification, regression, clustering and collaborative filtering, as well as an underlying gradient descent optimization primitive.
Available Methods
The following links provide a detailed explanation of the methods and usage examples for each of them:
- Classification and Regression
- Binary Classification
- SVM (L1 and L2 regularized)
- Logistic Regression (L1 and L2 regularized)
- Linear Regression
- Least Squares
- Lasso
- Ridge Regression
- Binary Classification
- Clustering
- k-Means
- Collaborative Filtering
- Matrix Factorization using Alternating Least Squares
- Optimization
- Gradient Descent and Stochastic Gradient Descent
- Linear Algebra
- Singular Value Decomposition
Dependencies
MLlib uses the jblas linear algebra library, which itself depends on native Fortran routines. You may need to install the gfortran runtime library if it is not already present on your nodes. MLlib will throw a linking error if it cannot detect these libraries automatically.
To use MLlib in Python, you will need NumPy version 1.7 or newer and Python 2.7.