Ranking Example - Catboost Typing CST Test
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Ranking Example — Catboost Code
Using CatBoost for a simple ranking problem.
from catboost import CatBoostRanker
import numpy as np
# Sample ranking data
X_train = np.random.rand(6,3)
y_train = np.array([1,2,3,1,2,3])
group_id = np.array([0,0,0,1,1,1])
# Define model
model = CatBoostRanker(iterations=100, learning_rate=0.1, depth=3, verbose=0)
# Train model
model.fit(X_train, y_train, group_id=group_id)
# Predict
y_pred = model.predict(X_train)
print('Ranking Predictions:', y_pred)Catboost Language Guide
CatBoost (Categorical Boosting) is an open-source gradient boosting library developed by Yandex, optimized for handling categorical features automatically and providing state-of-the-art performance for classification, regression, and ranking tasks.
Primary Use Cases
- ▸Binary and multiclass classification
- ▸Regression problems
- ▸Learning-to-rank tasks
- ▸Handling datasets with categorical features
- ▸Integration into machine learning pipelines for tabular data
Notable Features
- ▸Native support for categorical features
- ▸Ordered boosting to prevent overfitting
- ▸Supports GPU and CPU training
- ▸Efficient for large-scale datasets
- ▸Provides model interpretation tools
Origin & Creator
CatBoost was developed by Yandex in 2017 to provide a gradient boosting framework that efficiently handles categorical data while reducing prediction bias and overfitting.
Industrial Note
CatBoost is widely used in finance, recommendation systems, advertising, and other domains where tabular data contains categorical features and high predictive accuracy is needed.