python-scikit-learnRandom forest example
import numpy as np
from sklearn import datasets, ensemble, model_selection
X, y = datasets.load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)
model = ensemble.RandomForestClassifier()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)ctrl + cfrom sklearn importimport module from scikit-learn | load_irisloads Iris dataset |
model_selection.train_test_splitsplits given | .RandomForestClassifier(creates random forest classification model |
.fit(train model with a given features and target variable dataset | .predict(predict target variable based on given features dataset |
Usage example
import numpy as np
from sklearn import datasets, ensemble, model_selection
X, y = datasets.load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y)
model = ensemble.RandomForestClassifier()
model.fit(X_train, y_train)
print(model.score(X_test, y_test))output
1.0
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