Learn Catboost - 10 Code Examples & CST Typing Practice Test

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.

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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 with Grid Search CatBoost Save and Load Model

Learn CATBOOST with Real Code Examples

Updated Nov 24, 2025

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Code Sample Descriptions

CatBoost Simple Classification Example

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

# 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)

# Predict
y_pred = model.predict(X_test)
print('Accuracy:', accuracy_score(y_test, y_pred))

A minimal CatBoost example performing classification on the Iris dataset.

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CatBoost Regression Example

from catboost import CatBoostRegressor
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

# Generate data
X, y = make_regression(n_samples=200, n_features=5, noise=0.1, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Define model
model = CatBoostRegressor(iterations=200, learning_rate=0.05, depth=4, verbose=0)

# Train model
model.fit(X_train, y_train)

# Predict
y_pred = model.predict(X_test)
print('MSE:', mean_squared_error(y_test, y_pred))

A simple regression using CatBoost on synthetic data.

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CatBoost Multi-class Classification

from catboost import CatBoostClassifier
from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Load dataset
data = load_wine()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.3, random_state=42)

# Define model
model = CatBoostClassifier(iterations=150, learning_rate=0.1, depth=5, verbose=0, loss_function='MultiClass')

# Train model
model.fit(X_train, y_train)

# Predict
y_pred = model.predict(X_test)
print('Accuracy:', accuracy_score(y_test, y_pred))

CatBoost handling multi-class classification on the Wine dataset.

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CatBoost with Categorical Features

from catboost import CatBoostClassifier, Pool
import pandas as pd

# Sample data
data = pd.DataFrame({
    'feature_num': [1,2,3,4,5,6],
    'feature_cat': ['A','B','A','B','C','C'],
    'label': [0,1,0,1,0,1]
})
X = data[['feature_num','feature_cat']]
y = data['label']

# Define categorical features
cat_features = ['feature_cat']

# Create Pool
data_pool = Pool(X, y, cat_features=cat_features)

# Define model
model = CatBoostClassifier(iterations=50, learning_rate=0.1, depth=3, verbose=0)

# Train model
model.fit(data_pool)

# Predict
y_pred = model.predict(X)
print('Predictions:', y_pred)

Using CatBoost with categorical features in a classification task.

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CatBoost with Early Stopping

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_valid, y_train, y_valid = train_test_split(data.data, data.target, test_size=0.2, random_state=42)

# Define model
model = CatBoostClassifier(iterations=500, learning_rate=0.05, depth=4, verbose=0)

# Train with early stopping
model.fit(X_train, y_train, eval_set=(X_valid, y_valid), early_stopping_rounds=20)

# Predict
y_pred = model.predict(X_valid)
print('Predictions:', y_pred)

CatBoost training with early stopping based on validation set.

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CatBoost Ranking Example

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)

Using CatBoost for a simple ranking problem.

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CatBoost with Custom Loss Function

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)

# Custom loss function (logloss)
model = CatBoostClassifier(loss_function='Logloss', iterations=200, learning_rate=0.05, depth=4, verbose=0)

# Train model
model.fit(X_train, y_train)

# Predict
y_pred = model.predict(X_test)
print('Predictions:', y_pred)

Example showing how to use a custom loss function with CatBoost.

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CatBoost Feature Importance

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)

Compute and display feature importance using CatBoost.

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CatBoost with Grid Search

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_)

Performing hyperparameter tuning using Grid Search with CatBoost.

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CatBoost Save and Load Model

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)

# Save model
model.save_model('catboost_model.cbm')

# Load model
loaded_model = CatBoostClassifier()
loaded_model.load_model('catboost_model.cbm')

# Predict
y_pred = loaded_model.predict(X_test)
print('Accuracy:', y_pred)

Saving and loading a trained CatBoost model.

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Frequently Asked Questions about Catboost

What is Catboost?

What are the primary use cases for Catboost?

Binary and multiclass classification. Regression problems. Learning-to-rank tasks. Handling datasets with categorical features. Integration into machine learning pipelines for tabular data

What are the strengths of Catboost?

Excellent handling of categorical features. Reduced overfitting due to ordered boosting. High predictive accuracy. GPU acceleration for faster training. Easy integration with Python and ML pipelines

What are the limitations of Catboost?

Slower training on extremely large datasets compared to LightGBM. Less memory-efficient than LightGBM in some scenarios. Parameter tuning is important for optimal performance. Less suited for unstructured data like images or text. Some advanced features are only accessible via Python or CLI

How can I practice Catboost typing speed?

CodeSpeedTest offers 10+ real Catboost code examples for typing practice. You can measure your WPM, track accuracy, and improve your coding speed with guided exercises.

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Code Sample Descriptions

CatBoost Simple Classification Example

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

# 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)

# Predict
y_pred = model.predict(X_test)
print('Accuracy:', accuracy_score(y_test, y_pred))

A minimal CatBoost example performing classification on the Iris dataset.

Let’s Try →

CatBoost Regression Example

from catboost import CatBoostRegressor
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

# Generate data
X, y = make_regression(n_samples=200, n_features=5, noise=0.1, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Define model
model = CatBoostRegressor(iterations=200, learning_rate=0.05, depth=4, verbose=0)

# Train model
model.fit(X_train, y_train)

# Predict
y_pred = model.predict(X_test)
print('MSE:', mean_squared_error(y_test, y_pred))

A simple regression using CatBoost on synthetic data.

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CatBoost Multi-class Classification

from catboost import CatBoostClassifier
from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Load dataset
data = load_wine()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.3, random_state=42)

# Define model
model = CatBoostClassifier(iterations=150, learning_rate=0.1, depth=5, verbose=0, loss_function='MultiClass')

# Train model
model.fit(X_train, y_train)

# Predict
y_pred = model.predict(X_test)
print('Accuracy:', accuracy_score(y_test, y_pred))

CatBoost handling multi-class classification on the Wine dataset.

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CatBoost with Categorical Features

from catboost import CatBoostClassifier, Pool
import pandas as pd

# Sample data
data = pd.DataFrame({
    'feature_num': [1,2,3,4,5,6],
    'feature_cat': ['A','B','A','B','C','C'],
    'label': [0,1,0,1,0,1]
})
X = data[['feature_num','feature_cat']]
y = data['label']

# Define categorical features
cat_features = ['feature_cat']

# Create Pool
data_pool = Pool(X, y, cat_features=cat_features)

# Define model
model = CatBoostClassifier(iterations=50, learning_rate=0.1, depth=3, verbose=0)

# Train model
model.fit(data_pool)

# Predict
y_pred = model.predict(X)
print('Predictions:', y_pred)

Using CatBoost with categorical features in a classification task.

Let’s Try →

CatBoost with Early Stopping

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_valid, y_train, y_valid = train_test_split(data.data, data.target, test_size=0.2, random_state=42)

# Define model
model = CatBoostClassifier(iterations=500, learning_rate=0.05, depth=4, verbose=0)

# Train with early stopping
model.fit(X_train, y_train, eval_set=(X_valid, y_valid), early_stopping_rounds=20)

# Predict
y_pred = model.predict(X_valid)
print('Predictions:', y_pred)

CatBoost training with early stopping based on validation set.

Let’s Try →

CatBoost Ranking Example

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)

Using CatBoost for a simple ranking problem.

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CatBoost with Custom Loss Function

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)

# Custom loss function (logloss)
model = CatBoostClassifier(loss_function='Logloss', iterations=200, learning_rate=0.05, depth=4, verbose=0)

# Train model
model.fit(X_train, y_train)

# Predict
y_pred = model.predict(X_test)
print('Predictions:', y_pred)

Example showing how to use a custom loss function with CatBoost.

Let’s Try →

CatBoost Feature Importance

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)

Compute and display feature importance using CatBoost.

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CatBoost with Grid Search

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_)

Performing hyperparameter tuning using Grid Search with CatBoost.

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CatBoost Save and Load Model

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)

# Save model
model.save_model('catboost_model.cbm')

# Load model
loaded_model = CatBoostClassifier()
loaded_model.load_model('catboost_model.cbm')

# Predict
y_pred = loaded_model.predict(X_test)
print('Accuracy:', y_pred)

Saving and loading a trained CatBoost model.

Let’s Try →

Frequently Asked Questions about Catboost

What is Catboost?

What are the primary use cases for Catboost?

Binary and multiclass classification. Regression problems. Learning-to-rank tasks. Handling datasets with categorical features. Integration into machine learning pipelines for tabular data

What are the strengths of Catboost?

Excellent handling of categorical features. Reduced overfitting due to ordered boosting. High predictive accuracy. GPU acceleration for faster training. Easy integration with Python and ML pipelines

What are the limitations of Catboost?

How can I practice Catboost typing speed?

CodeSpeedTest offers 10+ real Catboost code examples for typing practice. You can measure your WPM, track accuracy, and improve your coding speed with guided exercises.