Class: Spark::Mllib::LinearRegressionModel

Inherits:
RegressionModel show all
Defined in:
lib/spark/mllib/regression/linear.rb

Overview

LinearRegressionModel

Train a linear regression model with no regularization using Stochastic Gradient Descent. This solves the least squares regression formulation

f(weights) = 1/n ||A weights-y||^2^

(which is the mean squared error). Here the data matrix has n rows, and the input RDD holds the set of rows of A, each with its corresponding right hand side label y. See also the documentation for the precise formulation.

Examples:

Spark::Mllib.import

# Dense vectors
data = [
  LabeledPoint.new(0.0, [0.0]),
  LabeledPoint.new(1.0, [1.0]),
  LabeledPoint.new(3.0, [2.0]),
  LabeledPoint.new(2.0, [3.0])
]
lrm = LinearRegressionWithSGD.train($sc.parallelize(data), initial_weights: [1.0])

lrm.intercept # => 0.0
lrm.weights   # => [0.9285714285714286]

lrm.predict([0.0]) < 0.5
# => true

lrm.predict([1.0]) - 1 < 0.5
# => true

lrm.predict(SparseVector.new(1, {0 => 1.0})) - 1 < 0.5
# => true

# Sparse vectors
data = [
  LabeledPoint.new(0.0, SparseVector.new(1, {0 => 0.0})),
  LabeledPoint.new(1.0, SparseVector.new(1, {0 => 1.0})),
  LabeledPoint.new(3.0, SparseVector.new(1, {0 => 2.0})),
  LabeledPoint.new(2.0, SparseVector.new(1, {0 => 3.0}))
]
lrm = LinearRegressionWithSGD.train($sc.parallelize(data), initial_weights: [1.0])

lrm.intercept # => 0.0
lrm.weights   # => [0.9285714285714286]

lrm.predict([0.0]) < 0.5
# => true

lrm.predict(SparseVector.new(1, {0 => 1.0})) - 1 < 0.5
# => true

Instance Attribute Summary

Attributes inherited from RegressionModel

#intercept, #weights

Method Summary

Methods inherited from RegressionModel

#initialize, #predict

Constructor Details

This class inherits a constructor from Spark::Mllib::RegressionModel