Machine Learning with R Caret – Part 1

(This article was first published on R Programming – DataScience+, and kindly contributed to R-bloggers) This blog post series is on machine learning with R. We will use the Caret package in R. In this part, we will first perform exploratory Data Analysis (EDA) on a real-world dataset, and then apply non-regularized linear regression to…

via Machine Learning with R Caret – Part 1 — R-bloggers

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Regresión logística y datos con grupos linealmente separables

Recientemente, los participantes en una encuesta realizada por la página web kaggle afirmaban que el modelo de regresión logística era la técnica estadística más utilizada en ciencia de datos. Este modelo proporciona una regla lineal de clasificación. Sin embargo, si tratamos de ajustar un modelo de regresión logística a datos con dos grupos linealmente separables (en principio, […]

via Regresión logística y datos con grupos linealmente separables — Caminos aleatorios

Regresión ridge (regresión contraída) con R

(This article was first published on R – Real Data, and kindly contributed to R-bloggers) In my last post Which linear model is best? I wrote about using stepwise selection as a method for selecting linear models, which turns out to have some issues (see this article, and Wikipedia). This post will be about two…

via Ridge Regression and the Lasso — R-bloggers

ModernDive: A free introduction to statistics and data science with R

(This article was first published on Revolutions, and kindly contributed to R-bloggers) If you’re thinking about teaching a course on statistics and data science using R, Chester Ismay and Albert Kim have created an online, open-source textbook for just that purpose. ModernDive is a textbook for that instructs students how to: use R to explore and visualize…

via ModernDive: A free introduction to statistics and data science with R — R-bloggers

Updates to the ‘forecast’ package for R

(This article was first published on Revolutions, and kindly contributed to R-bloggers) The forecast package for R, created and maintained by Professor Rob Hyndman of Monash University, is one of the more useful R packages available available on CRAN. Statistical forecasting — the process of predicting the future value of a time series — is…

via Updates to the ‘forecast’ package for R — R-bloggers