Evaluation Metrics for Regression Models: Interactive Guide
Learn MAE, MSE, RMSE, max error, R² and adjusted R² by playing: drag an outlier and watch every metric react, then compute them with scikit-learn.
Ridge Regression in R: Interactive Guide with a λ Playground
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.
Multiple Linear Regression in Python: Interactive Guide with a Model Builder
Learn multiple linear regression by building models: switch features on and off, watch R² vs adjusted R², learn feature selection and code it in Python.
Introduction to Regression: Interactive Guide with Games and Quiz
Learn regression basics by playing: dependent vs independent variables, bias and variance, residuals and the regression family, ending with a boss quiz.
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().
Elastic Net Regression in R: Interactive Guide with an α–λ Playground
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.
Generalized Linear Regression (GLM) in Python: Interactive Guide
Learn generalized linear models by playing: the three GLM components, matching outcomes to Normal, Binomial, Poisson and Gamma families, and a link-function lab.
Evaluation Metrics for Classification Models: Interactive Guide
Learn the confusion matrix, accuracy, precision, recall, specificity, F1, MCC, log loss and ROC AUC by playing with a live threshold and ROC curve on real data.
Bayesian Linear Regression in Python: Interactive Guide
Learn Bayesian linear regression by playing: Bayesian vs frequentist, Bayes’ theorem, and a live prior-to-posterior playground, with PyMC code.
Linear Regression in Python: Interactive Guide with a Line-Fitting Game
Learn linear regression by fitting the line yourself: drag the slope and intercept, do the maths, check the assumptions and code it in Python.
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