class Spark::Mllib::LinearRegressionWithSGD
Constants
- DEFAULT_OPTIONS
Public Class Methods
Train a linear regression model on the given data.
Parameters:¶ ↑
- rdd
-
The training data (
RDD
instance). - iterations
-
The number of iterations (default: 100).
- step
-
The step parameter used in SGD (default: 1.0).
- mini_batch_fraction
-
Fraction of data to be used for each SGD iteration (default: 1.0).
- initial_weights
-
The initial weights (default: nil).
- reg_param
-
The regularizer parameter (default: 0.0).
- reg_type
-
The type of regularizer used for training our model (default: nil).
Allowed values:
-
“l1” for using L1 regularization (lasso),
-
“l2” for using L2 regularization (ridge),
-
None for no regularization
-
- intercept
-
Boolean parameter which indicates the use or not of the augmented representation for training data (i.e. whether bias features are activated or not). (default: false)
- validate
-
Boolean parameter which indicates if the algorithm should validate data before training. (default: true)
Spark::Mllib::RegressionMethodBase::train
# File lib/spark/mllib/regression/linear.rb, line 114 def self.train(rdd, options={}) super weights, intercept = Spark.jb.call(RubyMLLibAPI.new, 'trainLinearRegressionModelWithSGD', rdd, options[:iterations].to_i, options[:step].to_f, options[:mini_batch_fraction].to_f, options[:initial_weights], options[:reg_param].to_f, options[:reg_type], options[:intercept], options[:validate]) LinearRegressionModel.new(weights, intercept) end