Feature Importance - Catboost Typing CST Test

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Feature Importance — Catboost Code

Compute and display feature importance using CatBoost.

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

# 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(iterations=100, learning_rate=0.1, depth=3, verbose=0)

# Train model
model.fit(X_train, y_train)

# Feature importance
importance = model.get_feature_importance()
print('Feature Importance:', importance)

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 with Grid Search CatBoost Save and Load Model

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Feature Importance — Catboost Code

Compute and display feature importance using CatBoost.

from catboost import CatBoostClassifier from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split # 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(iterations=100, learning_rate=0.1, depth=3, verbose=0) # Train model model.fit(X_train, y_train) # Feature importance importance = model.get_feature_importance() print('Feature Importance:', importance)

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.