How to use CatBoost Classifier and Regressor in Python?
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How to use CatBoost Classifier and Regressor in Python?

How to use CatBoost Classifier and Regressor in Python?

This recipe helps you use CatBoost Classifier and Regressor in Python

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Recipe Objective

Have you ever tried to use catboost models ie. regressor or classifier. In this we will using both for different dataset.

So this recipe is a short example of how we can use CatBoost Classifier and Regressor in Python.

Step 1 - Import the library

from sklearn import datasets from sklearn import metrics from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt import seaborn as sns plt.style.use("ggplot") import catboost as ctb

Here we have imported various modules like datasets, CatBoost and test_train_split from differnt libraries. We will understand the use of these later while using it in the in the code snipet.
For now just have a look on these imports.

Step 2 - Setup the Data for classifier

Here we have used datasets to load the inbuilt iris dataset and we have created objects X and y to store the data and the target value respectively. dataset = datasets.load_iris() X = dataset.data; y = dataset.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30)

Step 3 - Model and its Score

Here, we are using CatBoostClassifier as a Machine Learning model to fit the data. model_CBC = ctb.CatBoostClassifier() model_CBC.fit(X_train, y_train) print(model_CBC) Now we have predicted the output by passing X_test and also stored real target in expected_y. expected_y = y_test predicted_y = model_CBC.predict(X_test) Here we have printed classification report and confusion matrix for the classifier. print(metrics.classification_report(expected_y, predicted_y)) print(metrics.confusion_matrix(expected_y, predicted_y))

Step 4 - Setup the Data for regressor

Here we have used datasets to load the inbuilt boston dataset and we have created objects X and y to store the data and the target value respectively. dataset = datasets.load_boston() X = dataset.data; y = dataset.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30)

Step 5 - Model and its Score

Here, we are using CatBoostRegressor as a Machine Learning model to fit the data. model_CBR = ctb.CatBoostRegressor() model_CBR.fit(X_train, y_train) print(model_CBR) Now we have predicted the output by passing X_test and also stored real target in expected_y. expected_y = y_test predicted_y = model_CBR.predict(X_test) Here we have printed r2 score and mean squared log error for the Regressor. print(metrics.r2_score(expected_y, predicted_y)) print(metrics.mean_squared_log_error(expected_y, predicted_y)) plt.figure(figsize=(10,10)) sns.regplot(expected_y, predicted_y, fit_reg=True, scatter_kws={"s": 100})

As an output we get:

Learning rate set to 0.070767
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992:	learn: 0.0049991	total: 686ms	remaining: 4.83ms
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994:	learn: 0.0049853	total: 687ms	remaining: 3.45ms
995:	learn: 0.0049810	total: 688ms	remaining: 2.76ms
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997:	learn: 0.0049709	total: 689ms	remaining: 1.38ms
998:	learn: 0.0049663	total: 690ms	remaining: 690us
999:	learn: 0.0049625	total: 690ms	remaining: 0us



              precision    recall  f1-score   support

           0       1.00      1.00      1.00        15
           1       1.00      1.00      1.00        12
           2       1.00      1.00      1.00        11

    accuracy                           1.00        38
   macro avg       1.00      1.00      1.00        38
weighted avg       1.00      1.00      1.00        38


[[15  0  0]
 [ 0 12  0]
 [ 0  0 11]]
Learning rate set to 0.03284
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1:	learn: 8.9669310	total: 4.42ms	remaining: 2.21s
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