jcorpac ML Supervised Patterns
Supervised learning is the core of predictive modeling. This skill focuses on building robust, scalable pipelines using Scikit-Learn.
Pipeline Architecture
- Imputation: Handling missing values (
SimpleImputer). - Encoding: Transforming categorical data (
OneHotEncoder). - Scaling: Normalizing numerical features (
StandardScaler). - Estimator: The model (e.g.,
RandomForestClassifier,XGBRegressor).
Best Practices
- Never Leak Data: Split into train/test before any preprocessing.
- Cross-Validation: Use
cross_val_scoreorGridSearchCVto ensure generalization. - Fairness: Check for bias in training data.
Tools
Scikit-Learn: The industry standard for tabular data.XGBoost/LightGBM: High-performance gradient boosting.