[Verse 1] When data comes with labels that we know Classification sorts where samples go Regression finds the numbers in between Supervised learning trains on what we've seen Split your data first before you start Training set to learn, test set apart Cross validation keeps your models clean K-fold splits show what your metrics really mean [Chorus] Linear, trees, and neighbors too SVM with kernels breaking through Accuracy, precision, recall, F-one XGBoost, Random Forest, get it done Feature engineering makes it shine Scale and encode to draw the line Tune those hyperparameters right Supervised learning burning bright [Verse 2] Decision trees split data node by node Random forests where multiple trees have grown Gradient boosting like XGBoost and Light Instance-based kNN finds neighbors in sight Support vectors find the margin that's most wide Kernel tricks map features to the other side Linear regression draws the best-fit line Logistic curves for classification time [Chorus] Linear, trees, and neighbors too SVM with kernels breaking through Accuracy, precision, recall, F-one XGBoost, Random Forest, get it done Feature engineering makes it shine Scale and encode to draw the line Tune those hyperparameters right Supervised learning burning bright [Bridge] Watch for overfitting when your training's too perfect Underfitting means your model's not complex Data leakage sneaks future into past Grid search and random search make tuning fast RMSE and MAE for regression scores R-squared tells you what your model explores AUC-ROC curves show classification might Stratified folds keep your classes balanced right [Verse 3] Feature selection picks the best attributes Scaling normalizes all your input routes Missing value imputation fills the gaps Encoding turns categories into maps Time series splits respect the temporal flow Bayesian optimization helps your tuning grow Credit risk classifier as your final test Engineering pipelines put skills to the test [Chorus] Linear, trees, and neighbors too SVM with kernels breaking through Accuracy, precision, recall, F-one XGBoost, Random Forest, get it done Feature engineering makes it shine Scale and encode to draw the line Tune those hyperparameters right Supervised learning burning bright [Outro] From features raw to models that predict Supervised learning is the perfect pick When labels guide the learning of your code You'll find the patterns hidden in your load
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