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Documentation Index

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Overview

The training pipeline evaluates five classification algorithms using stratified k-fold cross-validation to select the best performing model based on ROC AUC score.

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Model Types

Five models are trained and compared:
  1. Logistic Regression: Linear baseline model
  2. K-Nearest Neighbors (KNN): Distance-based classifier
  3. Support Vector Machine (SVM): Kernel-based separator
  4. Decision Tree: Rule-based hierarchical model
  5. Random Forest: Ensemble of decision trees

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build_models()

Creates configured instances of all five models. Implementation: src/train.py:58 
def build_models(config: dict) -> dict:
    seed = int(config["seed"])
    models = {
        "Logistic Regression": LogisticRegression(
            max_iter=int(config["models"]["logistic_regression"]["max_iter"]),
            random_state=seed,
        ),
        "KNN": KNeighborsClassifier(
            n_neighbors=int(config["models"]["knn"]["n_neighbors"])
        ),
        "SVM": SVC(
            C=float(config["models"]["svm"]["C"]),
            kernel=config["models"]["svm"]["kernel"],
            gamma=config["models"]["svm"]["gamma"],
            probability=True,
            random_state=seed,
        ),
        "Decision Tree": DecisionTreeClassifier(
            max_depth=int(config["models"]["decision_tree"]["max_depth"]),
            min_samples_leaf=int(config["models"]["decision_tree"]["min_samples_leaf"]),
            random_state=seed,
        ),
        "Random Forest": RandomForestClassifier(
            n_estimators=int(config["models"]["random_forest"]["n_estimators"]),
            min_samples_leaf=int(config["models"]["random_forest"]["min_samples_leaf"]),
            random_state=seed,
            n_jobs=-1,
        ),
    }
    if test_mode_enabled():
        return {
            "Logistic Regression": models["Logistic Regression"],
            "Random Forest": RandomForestClassifier(
                n_estimators=min(50, int(config["models"]["random_forest"]["n_estimators"])),
                min_samples_leaf=int(config["models"]["random_forest"]["min_samples_leaf"]),
                random_state=seed,
                n_jobs=1,
            ),
        }
    return models

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Model Configuration

All hyperparameters are defined in config.yaml: 
models:
  logistic_regression:
    max_iter: 2000
  knn:
    n_neighbors: 7
  svm:
    C: 1.0
    kernel: rbf
    gamma: scale
  decision_tree:
    max_depth: 8
    min_samples_leaf: 10
  random_forest:
    n_estimators: 400
    min_samples_leaf: 2

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Hyperparameter Explanations

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Logistic Regression

  • max_iter: Maximum iterations for convergence (2000)

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K-Nearest Neighbors

  • n_neighbors: Number of neighbors to consider (7)

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Support Vector Machine

  • C: Regularization parameter (1.0)
  • kernel: Kernel type - rbf (radial basis function)
  • gamma: Kernel coefficient - scale (auto-computed)
  • probability: Enable probability estimates (required for predict_proba)

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Decision Tree

  • max_depth: Maximum tree depth (8)
  • min_samples_leaf: Minimum samples per leaf node (10)

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Random Forest

  • n_estimators: Number of trees in forest (400)
  • min_samples_leaf: Minimum samples per leaf node (2)
  • n_jobs: Parallel jobs (-1 = use all CPUs)

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Cross-Validation Setup

Stratified k-fold cross-validation with 5 splits. Implementation: src/train.py:126-142 
cv_splits = int(config["cv"]["n_splits"])
if test_mode_enabled():
    cv_splits = max(2, min(3, cv_splits))

cv = StratifiedKFold(
    n_splits=cv_splits,
    shuffle=True,
    random_state=int(config["seed"]),
)
scoring = {"roc_auc": "roc_auc", "precision": "precision", "recall": "recall", "f1": "f1"}

cv_rows = []
trained = {}

for name, model in models.items():
    pipe = Pipeline(steps=[("preprocessor", preprocessor), ("model", model)])
    scores = cross_validate(pipe, X_train, y_train, cv=cv, scoring=scoring, n_jobs=1 if test_mode_enabled() else -1)
    cv_rows.append(
        {
            "model": name,
            "cv_roc_auc_mean": float(np.mean(scores["test_roc_auc"])),
            "cv_precision_mean": float(np.mean(scores["test_precision"])),
            "cv_recall_mean": float(np.mean(scores["test_recall"])),
            "cv_f1_mean": float(np.mean(scores["test_f1"])),
        }
    )
    pipe.fit(X_train, y_train)
    trained[name] = pipe

​
Stratified K-Fold

Why Stratified?
  • Maintains class distribution in each fold
  • Critical for imbalanced datasets
  • Ensures each fold has representative samples of both classes (purchased=0 and purchased=1)
Configuration (config.yaml): 
cv:
  n_splits: 5

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Evaluation Metrics

Four metrics are computed for each fold:
  1. ROC AUC: Area under ROC curve (primary metric)
  2. Precision: True positives / (true positives + false positives)
  3. Recall: True positives / (true positives + false negatives)
  4. F1: Harmonic mean of precision and recall

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Model Selection Process

  1. Train all models with 5-fold cross-validation
  2. Compute mean scores across folds for each metric
  3. Rank by ROC AUC (primary metric)
  4. Select best model with highest mean ROC AUC
  5. Retrain on full training set
Implementation: src/train.py:155-157 
cv_df = pd.DataFrame(cv_rows).sort_values("cv_roc_auc_mean", ascending=False)
best_model_name = cv_df.iloc[0]["model"]
best_pipeline = trained[best_model_name]

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Preprocessing Pipeline

Each model is wrapped in a scikit-learn Pipeline with preprocessing. Implementation: src/train.py:34-55 
def build_preprocessor(X_train: pd.DataFrame, config: dict) -> ColumnTransformer:
    num_cols = X_train.select_dtypes(include=np.number).columns.tolist()
    cat_cols = X_train.select_dtypes(exclude=np.number).columns.tolist()

    numeric_transformer = Pipeline(
        steps=[
            ("scaler", StandardScaler()),
        ]
    )

    categorical_transformer = Pipeline(
        steps=[
            ("onehot", OneHotEncoder(handle_unknown="ignore")),
        ]
    )

    return ColumnTransformer(
        transformers=[
            ("num", numeric_transformer, num_cols),
            ("cat", categorical_transformer, cat_cols),
        ]
    )

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Preprocessing Steps

Numeric Features:
  • StandardScaler: Zero mean, unit variance normalization
Categorical Features:
  • OneHotEncoder: Convert categories to binary vectors
  • handle_unknown="ignore": Handle new categories in test data

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CV Results Format

Cross-validation results are stored in metrics.json: 
{
  "cv_ranking": [
    {
      "model": "Random Forest",
      "cv_roc_auc_mean": 0.892,
      "cv_precision_mean": 0.851,
      "cv_recall_mean": 0.723,
      "cv_f1_mean": 0.782
    },
    {
      "model": "Logistic Regression",
      "cv_roc_auc_mean": 0.876,
      "cv_precision_mean": 0.834,
      "cv_recall_mean": 0.698,
      "cv_f1_mean": 0.760
    }
  ]
}

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Usage Example

from src.train import build_models, build_preprocessor
from src.data import load_config, load_dataset, split_data
from sklearn.model_selection import cross_validate, StratifiedKFold

# Load data
config = load_config()
df = load_dataset(config)
X_train, X_test, y_train, y_test = split_data(df, config)

# Build models
models = build_models(config)
preprocessor = build_preprocessor(X_train, config)

# Cross-validate
cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
for name, model in models.items():
    pipe = Pipeline([("preprocessor", preprocessor), ("model", model)])
    scores = cross_validate(pipe, X_train, y_train, cv=cv, scoring="roc_auc")
    print(f"{name}: {scores['test_score'].mean():.3f}")

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Next Steps

Evaluation

Learn about model evaluation and threshold calibration

Feature Engineering

Understand features used by models

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