ML Classical ML

Purpose

Build supervised and unsupervised machine learning pipelines with scikit-learn, XGBoost, LightGBM, and CatBoost. Select models by problem type, tune hyperparameters systematically, validate with appropriate cross-validation, and handle class imbalance.

Agent Protocol

Trigger

Exact user phrases: "scikit-learn", "XGBoost", "LightGBM", "CatBoost", "regression", "classification", "clustering", "ensemble", "random forest", "gradient boosting", "SVM", "PCA", "feature importance", "cross-validation", "imbalanced data", "SMOTE", "hyperparameter tuning".

Input Context

Before activating, verify:

• Problem type (regression, binary classification, multiclass, clustering)
• Dataset size (rows, features, sparsity)
• Target distribution (balanced, imbalanced ratio)
• Feature types (numeric, categorical, text, datetime)
• Performance requirements (latency, throughput, memory)
• Interpretability needs (must explain predictions vs black-box OK)
• Existing baseline or prior experiments