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38 lines
1.4 KiB
38 lines
1.4 KiB
from sklearn.ensemble import RandomForestClassifier
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.linear_model import LogisticRegression
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.linear_model import SGDClassifier
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from sklearn import svm
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def RandomForest(X_train, X_test, y_train):
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random_forest = RandomForestClassifier(n_estimators=100,
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criterion='entropy',
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max_depth=10,
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min_samples_split=2,
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min_samples_leaf=1,
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random_state=0)
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random_forest.fit(X_train, y_train)
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return random_forest.predict(X_test), random_forest
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def KNN(X_train, X_test, y_train):
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knn = KNeighborsClassifier(n_neighbors=5)
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knn.fit(X_train, y_train)
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return knn.predict(X_test),knn
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def SVM(X_train, X_test, y_train):
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clf = svm.SVC(gamma=0.001)
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clf.fit(X_train,y_train)
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return clf.predict(X_test),clf
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def DecisionTree(X_train, X_test, y_train):
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decisionTree = DecisionTreeClassifier()
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decisionTree = decisionTree.fit(X_train,y_train)
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return decisionTree.predict(X_test),decisionTree
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def LogisticRegress(X_train, X_test, y_train):
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logistic = LogisticRegression()
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logistic.fit(X_train,y_train)
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return logistic.predict(X_test),logistic
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