catboost-first-tabular

CatBoost-First Strategy for Tabular Problems

Problem

When manual ML on tabular data is needed, many developers default to LightGBM or XGBoost (the "famous" frameworks from Kaggle winners). But for many tabular problems, CatBoost is the optimal choice, especially for small/medium datasets with categorical features.

Reality:

• CatBoost has native categorical feature handling (no need for target encoding)
• CatBoost has ordered boosting (prevents target leakage during training)
• CatBoost has robust default parameters (less hyperparameter tuning needed)
• CatBoost consistently outperforms LightGBM/XGBoost on small/medium tabular datasets with categorical features

Context / Trigger Conditions

Use this skill when:

• AutoGluon is not available or too slow (limited resources)
• Need to add CatBoost as a Silver signal in custom pipeline
• Manual GBDT baseline required (cannot use AutoGluon)
• Categorical features are dominant (>5-10 high-cardinality columns)
• High-cardinality categorical features (e.g., Neighborhood with 25+ categories, ZIP codes)
• Small dataset (<10K rows) where overfitting is a concern
• After trying LightGBM/XGBoost and getting worse results

Solution

Step 1: Try CatBoost First (Before LightGBM/XGBoost)

from catboost import CatBoostRegressor  # or CatBoostClassifier

# For regression
model = CatBoostRegressor(
    iterations=2500,
    learning_rate=0.02,
    depth=6,
    l2_leaf_reg=3.0,
    random_seed=42,
    verbose=0,
    early_stopping_rounds=50
)

# For classification (binary)
model = CatBoostClassifier(
    iterations=2500,
    learning_rate=0.02,
    depth=6,
    l2_leaf_reg=3.0,
    random_seed=42,
    verbose=0,
    early_stopping_rounds=50
)

# Train with 5-fold CV
from sklearn.model_selection import KFold
kf = KFold(n_splits=5, shuffle=True, random_state=42)
oof = np.zeros(len(X_train))
test_pred = np.zeros(len(X_test))
for fold, (trn_idx, val_idx) in enumerate(kf.split(X_train)):
    X_trn, X_val = X_train.iloc[trn_idx], X_train.iloc[val_idx]
    y_trn, y_val = y_train.iloc[trn_idx], y_train.iloc[val_idx]
    model.fit(X_trn, y_trn, eval_set=(X_val, y_val), verbose=0)
    oof[val_idx] = model.predict(X_val)
    test_pred += model.predict(X_test) / 5