ML Supervised Patterns

Supervised learning is the core of predictive modeling. This skill focuses on building robust, scalable pipelines using Scikit-Learn.

Pipeline Architecture

1. Imputation: Handling missing values (SimpleImputer).
2. Encoding: Transforming categorical data (OneHotEncoder).
3. Scaling: Normalizing numerical features (StandardScaler).
4. Estimator: The model (e.g., RandomForestClassifier, XGBRegressor).

Best Practices

• Never Leak Data: Split into train/test before any preprocessing.
• Cross-Validation: Use cross_val_score or GridSearchCV to ensure generalization.
• Fairness: Check for bias in training data.

Tools

• Scikit-Learn: The industry standard for tabular data.
• XGBoost / LightGBM: High-performance gradient boosting.