model-training

Model Training

Train, evaluate, and export ML models using scikit-learn pipelines with proper cross-validation and hyperparameter tuning.

When to Use

• User wants to train a classification or regression model
• User needs cross-validation scores for model selection
• User wants to tune hyperparameters with GridSearch or RandomizedSearch
• User needs to compare a model against a baseline
• User wants to save a trained model for deployment

Available Operations

1. Build a full sklearn Pipeline (preprocessing + model)
2. Run cross-validation with multiple scoring metrics
3. Tune hyperparameters with GridSearchCV or RandomizedSearchCV
4. Evaluate on held-out test set with classification_report / regression metrics
5. Compare against baseline (DummyClassifier/DummyRegressor)
6. Save the best model with joblib

Multi-Step Workflow

Step 1: Install Dependencies

pip install scikit-learn pandas numpy joblib

Step 2: Load Prepared Data

import pandas as pd
from sklearn.model_selection import train_test_split

df = pd.read_parquet("data/processed/dataset_clean.parquet")
X = df.drop(columns=["target"])
y = df["target"]

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y  # stratify for classification
)
print(f"Train: {X_train.shape}, Test: {X_test.shape}")
print(f"Class distribution:\n{y_train.value_counts(normalize=True)}")

Step 3: Build Preprocessing + Model Pipeline

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestClassifier

numeric_features = ["age", "salary", "experience"]
categorical_features = ["department", "role"]

preprocessor = ColumnTransformer(
    transformers=[
        ("num", StandardScaler(), numeric_features),
        ("cat", OneHotEncoder(handle_unknown="ignore"), categorical_features),
    ]
)

pipeline = Pipeline([
    ("preprocessor", preprocessor),
    ("classifier", RandomForestClassifier(random_state=42)),
])