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# Train Catboost Classifier with GridSearch

Catboostclassifier Python example with hyper parameter tuning. In this code snippet we train a classification model using Catboost. We initiate the model and then use grid search to to find optimum parameter values from a list that we define inside the grid dictionary. The model is then fit with these parameters assigned.

import catboost as cb #Create datasets train_dataset = cb.Pool(X_train,y_train, cat_features=categorical_indicies) eval_dataset = cb.Pool(X_val,y_val, cat_features=categorical_indicies) model = cb.CatBoostClassifier(iterations=1000, loss_function='Logloss', eval_metric='Accuracy') #Declare parameters to tune and values to try grid = {'learning_rate': [0.03, 0.1], 'depth': [4, 6, 10], 'l2_leaf_reg': [1, 3, 5,]} #Find optimum parameters model.grid_search(grid,train_dataset,plot=True) #Fit model with early stopping if improvement hasn't been made within 50 iterations model.fit(train_dataset, eval_set=eval_dataset, early_stopping_rounds=50, plot=True, silent=False)

By analyseup - Last Updated Jan. 10, 2022, 11:28 p.m.

Gpu - Regression - Catboost

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