Feature selection. In machine learning and statistics, feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features for use in model construction. The central assumption when using a feature selection technique is that the data contains many redundant or irrelevant features. Redundant features are those which provide no more information than the currently selected features, and irrelevant features provide no useful information in any context. Feature selection techniques are a subset of the more general field of feature extraction. Feature extraction creates new features from functions of the original features, whereas feature selection returns a subset of the features. Feature selection techniques are often used in domains where there are many features and comparatively few samples (or data points).
Improved model interpretability,shorter training times,enhanced generalisation by reducing overfitting. The. Penalized.pdf. Regression - Why does the Lasso provide Variable Selection? Lasso - What problem do shrinkage methods solve? Give a set of input measurements x1, x2 ...xp and an outcome measurement y, the lasso fits a linear model yhat=b0 + b1*x1+ b2*x2 + ... bp*xp The criterion it uses is: Minimize sum( (y-yhat)^2 ) subject to sum[absolute value(bj)] <= s The first sum is taken over observations (cases) in the dataset. However when for smaller values of s (s>=0) the solutions are shrunken versions of the least squares estimates. The computation of the lasso solutions is a quadratic programming problem, and can be tackled by standard numerical analysis algorithms. Least angle regression is like a more "democratic" version of forward stepwise regression. Forward stepwise regression algorithm: Start with all coefficients bj equal to zero.
Least angle regression algorithm: Start with all coefficients bj equal to zero. Forecast.pdf. CoursLasso.pdf. 1001.0188.pdf.