Regularización

Regresión ridge, lasso y elastic nets para modelos lineales

Para aumentar la precisión en conjuntos de datos de dimensiones bajas y medianas, implemente una regresión de mínimos cuadrados con regularización mediante lasso o ridge.

Para reducir el tiempo de cálculo en conjuntos de datos de altas dimensiones, ajuste un modelo de regresión lineal regularizada mediante fitrlinear.

Funciones

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Regularización sin utilizar ningún objeto

`lasso`	Lasso or elastic net regularization for linear models
`ridge`	Ridge regression
`lassoPlot`	Trace plot of lasso fit

Regularización utilizando funciones `RegressionLinear`

`fitrlinear`	Fit linear regression model to high-dimensional data
`predict`	Predict response of linear regression model

Objetos

`RegressionLinear`	Linear regression model for high-dimensional data
`RegressionPartitionedLinear`	Cross-validated linear regression model for high-dimensional data

Temas

Lasso Regularization
See how lasso identifies and discards unnecessary predictors.
Lasso and Elastic Net with Cross Validation
Predict the mileage (MPG) of a car based on its weight, displacement, horsepower, and acceleration using lasso and elastic net.
Wide Data via Lasso and Parallel Computing
Identify important predictors using lasso and cross-validation.
Lasso and Elastic Net
The lasso algorithm is a regularization technique and shrinkage estimator. The related elastic net algorithm is more suitable when predictors are highly correlated.
Ridge Regression
Ridge regression addresses the problem of multicollinearity (correlated model terms) in linear regression problems.