How to use Classification and Regression Tree in Python?
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How to use Classification and Regression Tree in Python?

How to use Classification and Regression Tree in Python?

This recipe helps you use Classification and Regression Tree in Python

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Recipe Objective

Have you ever tried to use Classification and Regression Tree. In this we will using both for different dataset.

So this recipe is a short example of how we can use Classification and Regression Tree in Python.

Step 1 - Import the library

from sklearn import datasets from sklearn import metrics from sklearn.tree import DecisionTreeClassifier from sklearn.tree import DecisionTreeRegressor from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt import seaborn as sns

Here we have imported various modules like datasets, DecisionTreeClassifier, DecisionTreeRegressor, test_train_split, etc 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 wine dataset and we have created objects X and y to store the data and the target value respectively. dataset = datasets.load_wine() 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 DecisionTreeClassifier as a Machine Learning model to fit the data. model = DecisionTreeClassifier() 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 DecisionTreeRegressor as a Machine Learning model to fit the data. model = DecisionTreeRegressor() 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 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))

As an output we get:

AdaBoostClassifier(algorithm="SAMME.R", base_estimator=None,
          learning_rate=1.0, n_estimators=50, random_state=None)

              precision    recall  f1-score   support

           0       1.00      1.00      1.00        11
           1       0.84      0.94      0.89        17
           2       0.93      0.82      0.87        17

   micro avg       0.91      0.91      0.91        45
   macro avg       0.93      0.92      0.92        45
weighted avg       0.92      0.91      0.91        45


[[11  0  0]
 [ 0 16  1]
 [ 0  3 14]]

AdaBoostRegressor(base_estimator=None, learning_rate=1.0, loss="linear",
         n_estimators=50, random_state=None)

0.753105739118306

0.03629416190915715

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