spark-instrumented-optimizer/python
Xiangrui Meng 32218307ed [SPARK-4372][MLLIB] Make LR and SVM's default parameters consistent in Scala and Python
The current default regParam is 1.0 and regType is claimed to be none in Python (but actually it is l2), while regParam = 0.0 and regType is L2 in Scala. We should make the default values consistent. This PR sets the default regType to L2 and regParam to 0.01. Note that the default regParam value in LIBLINEAR (and hence scikit-learn) is 1.0. However, we use average loss instead of total loss in our formulation. Hence regParam=1.0 is definitely too heavy.

In LinearRegression, we set regParam=0.0 and regType=None, because we have separate classes for Lasso and Ridge, both of which use regParam=0.01 as the default.

davies atalwalkar

Author: Xiangrui Meng <meng@databricks.com>

Closes #3232 from mengxr/SPARK-4372 and squashes the following commits:

9979837 [Xiangrui Meng] update Ridge/Lasso to use default regParam 0.01 cast input arguments
d3ba096 [Xiangrui Meng] change 'none' back to None
1909a6e [Xiangrui Meng] change default regParam to 0.01 and regType to L2 in LR and SVM
2014-11-13 13:54:16 -08:00
..
docs [SPARK-3870] EOL character enforcement 2014-10-31 12:39:52 -07:00
lib [SPARK-2305] [PySpark] Update Py4J to version 0.8.2.1 2014-07-29 19:02:06 -07:00
pyspark [SPARK-4372][MLLIB] Make LR and SVM's default parameters consistent in Scala and Python 2014-11-13 13:54:16 -08:00
test_support [SPARK-3634] [PySpark] User's module should take precedence over system modules 2014-09-24 12:10:09 -07:00
.gitignore [SPARK-3946] gitignore in /python includes wrong directory 2014-10-14 14:09:39 -07:00
run-tests [SPARK-4348] [PySpark] [MLlib] rename random.py to rand.py 2014-11-13 10:24:54 -08:00