scikit learn random forest classifier

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Christopher
22 Sep 2019
1from sklearn.ensemble import RandomForestClassifier
2
3
4clf = RandomForestClassifier(max_depth=2, random_state=0)
5
6clf.fit(X, y)
7
8print(clf.predict([[0, 0, 0, 0]]))
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Mariana
06 Oct 2019
1from sklearn.ensemble import RandomForestClassifier
2from sklearn.datasets import make_classification
3X, y = make_classification(n_samples=1000, n_features=4,
4                           n_informative=2, n_redundant=0,
5                           random_state=0, shuffle=False)
6clf = RandomForestClassifier(max_depth=2, random_state=0)
7clf.fit(X, y)
8
9print(clf.predict([[0, 0, 0, 0]]))
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Rafaela
29 Jan 2019
1from sklearn.ensemble import RandomForestClassifier
2from sklearn.datasets import make_classification
3
4
5X, y = make_classification(n_samples=1000, n_features=4,
6                            n_informative=2, n_redundant=0,
7                            random_state=0, shuffle=False)
8clf = RandomForestClassifier(max_depth=2, random_state=0)
9
10clf.fit(X, y)
11
12print(clf.predict([[0, 0, 0, 0]]))
Caterina
18 May 2017
1from sklearn.ensemble import BaggingClassifier
2from sklearn.neighbors import KNeighborsClassifier
3bagging = BaggingClassifier(KNeighborsClassifier(),
4                            max_samples=0.5, max_features=0.5)
Lisa
26 Sep 2017
1import matplotlib.pyplot as plt
2import seaborn as sns
3%matplotlib inline
4# Creating a bar plot
5sns.barplot(x=feature_imp, y=feature_imp.index)
6# Add labels to your graph
7plt.xlabel('Feature Importance Score')
8plt.ylabel('Features')
9plt.title("Visualizing Important Features")
10plt.legend()
11plt.show()
12
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