with Grid Search - Catboost Typing CST Test

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with Grid Search — Catboost Code

Performing hyperparameter tuning using Grid Search with CatBoost.

from catboost import CatBoostClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split, GridSearchCV

# Load dataset
data = load_iris()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=42)

# Define model
model = CatBoostClassifier(verbose=0)

# Define hyperparameter grid
param_grid = {'depth':[3,4,5], 'learning_rate':[0.05,0.1], 'iterations':[100,200]}

# Grid Search
grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=3)
grid_search.fit(X_train, y_train)
print('Best Params:', grid_search.best_params_)

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.

More Catboost Typing Exercises

CatBoost Simple Classification Example CatBoost Regression Example CatBoost Multi-class Classification CatBoost with Categorical Features CatBoost with Early Stopping CatBoost Ranking Example CatBoost with Custom Loss Function CatBoost Feature Importance CatBoost Save and Load Model

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with Grid Search — Catboost Code

Performing hyperparameter tuning using Grid Search with CatBoost.

from catboost import CatBoostClassifier from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split, GridSearchCV # Load dataset data = load_iris() X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=42) # Define model model = CatBoostClassifier(verbose=0) # Define hyperparameter grid param_grid = {'depth':[3,4,5], 'learning_rate':[0.05,0.1], 'iterations':[100,200]} # Grid Search grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=3) grid_search.fit(X_train, y_train) print('Best Params:', grid_search.best_params_)

Catboost Language Guide

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.