Machine Learning with R
Modern statistics has become almost synonymous with machine learning, a collection of techniques that utilize today's incredible computing power. This two-part course focuses on the available methods for implementing machine learning algorithms in R, and will examine some of the underlying theory behind the curtain. We start with the foundation of it all, the linear model. We look how to assess model quality with traditional measures and cross-validation and visualize models with coefficient plots. Next we turn to penalized regression with the Elastic Net. After that we turn to Boosted Decision Trees utilizing xgboost. Along the way we learn modern techniques for preprocessing data.
Outline/Structure of the Workshop
Learn about the best fit line
Understand the formula interface in R
Understand the design matrix
Fit Models with lm
Visualize the coefficients with coefplot
Learn about penalized regression with the Lasso and Ridge
Fit models with glmnet
Understand the coefficient path
View coefficients with coefplot
Boosted Decision Trees
Learn how to make classifications (and regression) using recursive partitioning
Fit models with xgboost
- Understand how to quickly and properly build design matrices for model training and prediction
- Learn how to fitted lasso and ridge penalized regression for automated feature selection with glmnet
- Learn how to fitted boosted trees and forests with xgboost
- Score new data
- Visualize your models
Data Science Enthusiasts who want to explore the power of R for ML
Prerequisites for Attendees
Attendees should have a good understanding of linear models and classification and should have R and RStudio installed, along with the tidymodels, glmnet, xgboost, boot, ggplot2, UsingR and coefplot packages.
schedule Submitted 4 years ago
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