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What is lasso in machine learning?
In statistics and machine learning, lasso (least absolute shrinkage and selection operator; also Lasso or LASSO) is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the resulting statistical model.
What is the lasso technique?
The Lasso is a shrinkage and selection method for linear regression. It minimizes the usual sum of squared errors, with a bound on the sum of the absolute values of the coefficients. It has connections to soft-thresholding of wavelet coefficients, forward stagewise regression, and boosting methods.
How to fit a GLM to lasso regularization?
fit a GLM with lasso or elasticnet regularization Fit a generalized linear model via penalized maximum likelihood. The regularization path is computed for the lasso or elasticnet penalty at a grid of values for the regularization parameter lambda. Can deal with all shapes of data, including very large sparse data matrices.
How to calculate lassoglm for a generalized linear model?
B = lassoglm (X,y) returns penalized, maximum-likelihood fitted coefficients for generalized linear models of the predictor data X and the response y, where the values in y are assumed to have a normal probability distribution. Each column of B corresponds to a particular regularization coefficient in Lambda.
How does lassoglm estimate the largest value of Lambda?
See Lasso. If you do not supply Lambda, then lassoglm estimates the largest value of Lambda that gives a nonnull model. In this case, LambdaRatio gives the ratio of the smallest to the largest value of the sequence, and NumLambda gives the length of the vector.
How to do lasso regularization with predictors and observations?
Create a random matrix X with 100 observations and 10 predictors. Create the normally distributed response y using only four of the predictors and a small amount of noise. Perform lasso regularization. Find the coefficient vector for the 75th Lambda value in B. lassoglm identifies and removes the redundant predictors.