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

How to use RandomForest Classifier and Regressor in Python?

This recipe helps you use RandomForest Classifier and Regressor in Python

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Recipe Objective

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

Step 1 - Import the library

from sklearn import datasets from sklearn import metrics from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import RandomForestRegressor 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, RandomForest 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_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 RandomForestClassifier as a Machine Learning model to fit the data. model = RandomForestClassifier() 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 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 RandomForestRegressor as a Machine Learning model to fit the data. model_RFR = RandomForestRegressor() model_RFR.fit(X_train, y_train) print(); print(model_RFR) 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_RFR.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:

RandomForestClassifier(bootstrap=True, class_weight=None, criterion="gini",
            max_depth=None, max_features="auto", 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=10, n_jobs=None,
            oob_score=False, random_state=None, verbose=0,
            warm_start=False)

              precision    recall  f1-score   support

           0       1.00      1.00      1.00        14
           1       1.00      0.95      0.97        20
           2       0.92      1.00      0.96        11

   micro avg       0.98      0.98      0.98        45
   macro avg       0.97      0.98      0.98        45
weighted avg       0.98      0.98      0.98        45


[[14  0  0]
 [ 0 19  1]
 [ 0  0 11]]

RandomForestRegressor(bootstrap=True, criterion="mse", max_depth=None,
           max_features="auto", 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=10, n_jobs=None,
           oob_score=False, random_state=None, verbose=0, warm_start=False)

0.8609661819993468

0.02760579419028312

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