class sklearn.ensemble.RandomForestClassifier(n_estimators=100, *, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_s...
(Implementation of Random Forest using Python Scikit-Learn) As I said before, it can be used for both classification and regression. There are two classes in the sklearn.ensemble library related to Random Forest. Import Random Forest class using the below code for different problems. 正如我之前...
from sklearn.ensembleimportRandomForestClassifier rf=RandomForestClassifier()rf.fit(X,y)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_s...
estimators and max_depth Documentationofsklearn RandomForestClassifier:https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html''' model=RandomForestClassifier()# fit the modelwiththe training data model.fit(train_x,train_y)# numberoftrees usedprint('Number of ...
ensemble import RandomForestRegressor from sklearn.metrics import mean_squared_error, r2_score np.random.seed(666) n, p = 1000, 10 # Generate some feature matrix X = np.random.normal(size=(n,p)) # Generate some simple feature response to predict Y = 0.5 * X[:, 0] + X[:, 1] ...
1fromsklearn.datasetsimportmake_moons2fromsklearn.model_selectionimporttrain_test_split3fromsklearn.linear_modelimportLogisticRegression4fromsklearn.ensembleimportRandomForestClassifier, VotingClassifier5fromsklearn.svmimportSVC6fromsklearn.metricsimportaccuracy_score78X, y = make_moons(n_samples=500, noise...
Enhances the prediction performance of Random Forest and XGBoost by combining their strengths and adopting a complementary diversification approach Supports parallel processing to ensure scalability Handles missing data by design Adopts scikit-learn API for the ease of use ...
DataFrame import matplotlib.pyplot as plt from sklearn.ensemble import RandomForestRegressor import sklearn.preprocessing as preprocessing from sklearn import linear_model from sklearn.model_selection import train_test_split data_train = pd.read_csv(r"C:\Users\Administrator\Desktop\titanic\train.csv"...
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