[Verse 1] Data flows like rivers through your pipeline clean Features raw and messy need a washing scene Encode the categories, scale the numbers bright Impute the missing pieces, make your dataset right Classification calls for classes you predict Regression seeks the values that your model picked [Chorus] Split your data, train and test apart Cross-validate to guard your beating heart Tune those hyperparameters with care Grid search, random search through parameter air F1, precision, recall your faithful friends RMSE and MAE where regression ends [Verse 2] Linear models draw their lines so straight and true Logistic curves through probability's debut Decision trees branch out with questions sharp and clear Random forests gather wisdom year by year XGBoost accelerates with gradients refined LightGBM speeds the learning in your mind [Chorus] Split your data, train and test apart Cross-validate to guard your beating heart Tune those hyperparameters with care Grid search, random search through parameter air F1, precision, recall your faithful friends RMSE and MAE where regression ends [Bridge] K-nearest neighbors vote on similarity's throne Support vector margins carve boundaries in stone Overfitting memorizes every single flaw Underfitting barely scratches what the patterns saw Data leakage whispers secrets from tomorrow's test Stratified folds keep your samples fairly blessed [Verse 3] Credit risk classifier needs XGBoost's might Feature engineering polished diamond bright AUC-ROC curves measure predictive power's range R-squared explains the variance you rearrange Bayesian optimization hunts the perfect score K-fold validation opens wisdom's door [Final Chorus] Split your data, train and test apart Cross-validation guards your model's heart Hyperparameter tuning finds the key Supervised learning sets your insights free Accuracy, precision, recall align RMSE, MAE, R-squared combine [Outro] From linear slopes to neural network dreams Supervised learning flows through data streams Classification, regression hand in hand Teaching machines to truly understand
← Unit 1.3 — Python & AI Tooling Ecosystem | Unit 2.2 — Unsupervised Learning →