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

How to use GradientBoosting Classifier and Regressor in Python?

This recipe helps you use GradientBoosting Classifier and Regressor in Python

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Recipe Objective

Have you ever tried to use GradientBoosting 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 GradientBoosting Classifier and Regressor in Python.

Step 1 - Import the library

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

Here we have imported various modules like datasets, GradientBoostingRegressor, GradientBoostingClassifier 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 cancer dataset and we have created objects X and y to store the data and the target value respectively. dataset = datasets.load_breast_cancer() X = dataset.data y = dataset.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)

Step 3 - Model and its Score

Here, we are using GradientBoostingClassifier as a Machine Learning model to fit the data. model = GradientBoostingClassifier() model.fit(X_train, y_train) print(model) 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.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=0.25)

Step 5 - Model and its Score

Here, we are using GradientBoostingRegressor as a Machine Learning model to fit the data. model = GradientBoostingRegressor() model.fit(X_train, y_train) print(); print(model) 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.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:

GradientBoostingClassifier(criterion="friedman_mse", init=None,
              learning_rate=0.1, loss="deviance", max_depth=3,
              max_features=None, max_leaf_nodes=None,
              min_impurity_decrease=0.0, min_impurity_split=None,
              min_samples_leaf=1, min_samples_split=2,
              min_weight_fraction_leaf=0.0, n_estimators=100,
              n_iter_no_change=None, presort="auto", random_state=None,
              subsample=1.0, tol=0.0001, validation_fraction=0.1,
              verbose=0, warm_start=False)

              precision    recall  f1-score   support

           0       0.98      0.91      0.94        45
           1       0.96      0.99      0.97        98

   micro avg       0.97      0.97      0.97       143
   macro avg       0.97      0.95      0.96       143
weighted avg       0.97      0.97      0.96       143


[[41  4]
 [ 1 97]]

GradientBoostingRegressor(alpha=0.9, criterion="friedman_mse", init=None,
             learning_rate=0.1, loss="ls", max_depth=3, max_features=None,
             max_leaf_nodes=None, min_impurity_decrease=0.0,
             min_impurity_split=None, min_samples_leaf=1,
             min_samples_split=2, min_weight_fraction_leaf=0.0,
             n_estimators=100, n_iter_no_change=None, presort="auto",
             random_state=None, subsample=1.0, tol=0.0001,
             validation_fraction=0.1, verbose=0, warm_start=False)

0.8347320926359012

0.02273902061395565

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