"LinearRegression" (Machine Learning Method)
- Method for Predict.
- Predict values using a linear combination of features.
Details & Suboptions
- The linear regression predicts the numerical output y using a linear combination of numerical features . The conditional probability is modeled according to , with .
- The estimation of the parameter vector θ is done by minimizing the loss function , where m is the number of examples and n is the number of numerical features.
- The following suboptions can be given:
"L1Regularization" 0 value of in the loss function "L2Regularization" Automatic value of iin the loss function "OptimizationMethod" Automatic what optimization method to use
- Possible settings for the "OptimizationMethod" option include:
"NormalEquation" linear algebra method "StochasticGradientDescent" stochastic gradient method "OrthantWiseQuasiNewton" orthant-wise quasi-Newton method
- For this method, Information[PredictorFunction[…],"Function"] gives a simple expression to compute the predicted value from the features.
Examplesopen allclose all
Basic Examples (2)
Train a predictor on labeled examples:
Look at the Information:
Generate two-dimensional data:
Train a predictor function on it:
Compare the data with the predicted values and look at the standard deviation: