Ml Model Trainer

Overview

Trains, evaluates, and serializes traditional machine learning models for tabular/structured data. Covers problem type identification, feature engineering checklist, model selection with tradeoffs, hyperparameter tuning (GridSearch, Optuna), cross-validation, evaluation metrics by problem type, full scikit-learn Pipeline, model serialization (joblib / pickle / ONNX), and a complete end-to-end training script template.

When to Use This Skill

• Building a predictive model on tabular data (CSV, database, features from other systems).
• The user has "a dataset with labels" or "predict X from these features".
• You need something interpretable and fast to train/infer (as opposed to deep learning or LLMs).

Prerequisites

• Cleaned tabular dataset (pandas DataFrame) with target variable.
• Python with scikit-learn, pandas, numpy, joblib. Optionally optuna, xgboost, lightgbm, catboost, pytorch.
• Understanding of the business problem (what "good" looks like).

Steps

1. Problem framing:

   • Classification (binary, multiclass, multilabel)?
   • Regression (continuous target)?
   • Clustering (no labels, find groups)?
   • Time-series specific considerations?

2. Feature engineering checklist:

   • Handle missing values (imputer in pipeline).
   • Encode categoricals (OneHot, Target, Ordinal — choose per cardinality).
   • Scale numeric features (StandardScaler, RobustScaler).
   • Create interactions, polynomial features, date/time features, aggregations.
   • Feature selection (mutual info, permutation importance, or model-based).

3. Model selection (with tradeoffs):

   • Baseline: Dummy / Linear / Logistic.
   • Tree-based: RandomForest, XGBoost, LightGBM, CatBoost (often win on tabular).
   • Linear models for interpretability / speed.
   • Neural nets (MLP or more advanced) only when data volume justifies.

4. Cross-validation & tuning:

   • StratifiedKFold for classification.
   • TimeSeriesSplit for time-ordered data.
   • GridSearchCV or (better) Optuna for hyperparameter search.
   • Nested CV when you need unbiased performance estimate.

5. Evaluation (choose the right metrics):

   • Classification: Accuracy, Precision/Recall/F1 (per class + macro), ROC-AUC, PR-AUC, confusion matrix, calibration.
   • Regression: MAE, RMSE, R², MAPE (be careful with zeros).
   • Clustering: Silhouette, Davies-Bouldin, or downstream task performance.

6. Full Pipeline:

   • Use sklearn.pipeline.Pipeline + ColumnTransformer so preprocessing is fitted only on train and applied consistently.
   • Include the final estimator.

7. Serialization & serving:

   • joblib.dump(pipeline, 'model.joblib').
   • ONNX for cross-platform / faster inference.
   • Simple FastAPI or Flask wrapper for real-time serving.
   • Batch scoring script.

8. Output:

   • Complete train.py that loads data → builds pipeline → tunes → evaluates → saves model + metrics.
   • Feature importance plot (for tree models).
   • Evaluation report (Markdown or notebook).
   • Inference example code.
   • Notes on monitoring (data drift, prediction drift).

Examples

A full end-to-end script for a customer churn classification problem (tabular features): preprocessing with ColumnTransformer, LightGBM + Optuna tuning, cross-validation, SHAP importance, model save, and a simple prediction function is included. Similar template for regression and clustering.

Edge Cases & Error Handling

• Class imbalance: Use class_weight, SMOTE (carefully), or appropriate metrics (not accuracy).
• Data leakage: Pipeline ensures no leakage; be extra careful with time-based features.
• Categorical with high cardinality: Target encoding with smoothing or embeddings.

Verification

1. The training script runs end-to-end and produces a saved model.
2. Cross-validation scores are reasonable and stable.
3. On a held-out test set, performance matches or exceeds the CV estimate.
4. The pipeline can be loaded in a fresh environment and used to predict on new data with identical preprocessing.
5. Feature importance or SHAP values make business sense.
6. Success: You have a validated, serialized model that can be deployed and monitored.

References

• scikit-learn User Guide
• Hands-On Machine Learning (book)
• Optuna
• XGBoost / LightGBM / CatBoost docs
• SHAP
• MLflow or DVC for experiment tracking