サクサク読めて、アプリ限定の機能も多数!
トップへ戻る
やる気の出し方
statweb.stanford.edu/~tibs
Stanford statistical learning software This is a collection of R packages written by current and former members of the labs of Trevor Hastie, Jon Taylor and Rob Tibshirani. All of these packages are actively supported by their authors. Lasso, elastic net and regularized modelling glmnet : Our most popular, and actively updated and maintained package. Extremely efficient procedures for fitting the
The Lasso Page L1-constrained fitting for statistics and data mining 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. A simple explanation of the las
このページを最初にブックマークしてみませんか?
『Rob Tibshirani』の新着エントリーを見る
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く