Polynomial Regression in R: Interactive Guide with a Degree Slider
Learn polynomial regression in R by turning the degree dial: watch training and test error on real data, spot overfitting and code it with lm().
Learn polynomial regression in R by turning the degree dial: watch training and test error on real data, spot overfitting and code it with lm().
Learn ridge regression in R by playing: a live λ playground on mtcars, Ridge vs Lasso rapid fire, glmnet code and a final boss quiz.
Learn Elastic Net regression in R by playing: mix Ridge and Lasso with alpha, slide lambda on real data, then fit and tune it with glmnet.
Learn decision trees by playing: entropy, Gini and information gain, ID3 vs C4.5 vs CART, and a live depth slider that shows overfitting, with rpart code in R.
Interactive R programming · Machine learning Lasso Regression in R, the game Learn Lasso by playing it. Seven short missions, a live λ playground built on real data, and a…
Ridge and Lasso regression are two powerful regularization techniques, but each comes with its own limitations: Ridge Regression (L2): Shrinks coefficients to prevent overfitting but never reduces them to zero…
Decision trees are one of the most basic and widely used machine learning algorithms, which fall under supervised machine learning techniques. Decision trees can handle both regression and classification tasks,…
Regularization is a crucial concept in machine learning used to prevent overfitting, which happens when a model performs well on the training data but poorly on unseen or testing data…
Regularization is a technique used in machine learning to reduce overfitting, which happens when a model performs well on the training data but poorly on the testing data due to…
In this article, we'll explore how polynomial regression can enhance our machine learning projects in R. While linear regression is a staple in machine learning, there are situations where polynomial…