65 lines
1.8 KiB
Python
65 lines
1.8 KiB
Python
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# importing necessary libraries
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from sklearn import datasets
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from sklearn.metrics import confusion_matrix
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from sklearn.model_selection import train_test_split
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# loading the iris dataset
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iris = datasets.load_iris()
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# X -> features, y -> label
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X = iris.data
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y = iris.target
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# dividing X, y into train and test data
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X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 0)
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# training a DescisionTreeClassifier
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from sklearn.tree import DecisionTreeClassifier
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dtree_model = DecisionTreeClassifier(max_depth = 2).fit(X_train, y_train)
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dtree_predictions = dtree_model.predict(X_test)
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# creating a confusion matrix
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cm = confusion_matrix(y_test, dtree_predictions)
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print("DescisionTreeClassifier", cm)
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# training a linear SVM classifier
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from sklearn.svm import SVC
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svm_model_linear = SVC(kernel = 'linear', C = 1).fit(X_train, y_train)
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svm_predictions = svm_model_linear.predict(X_test)
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# model accuracy for X_test
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accuracy = svm_model_linear.score(X_test, y_test)
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# creating a confusion matrix
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cm = confusion_matrix(y_test, svm_predictions)
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print("linear SVM: ", cm)
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# training a KNN classifier
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from sklearn.neighbors import KNeighborsClassifier
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knn = KNeighborsClassifier(n_neighbors = 7).fit(X_train, y_train)
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# accuracy on X_test
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accuracy = knn.score(X_test, y_test)
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print(accuracy)
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# creating a confusion matrix
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knn_predictions = knn.predict(X_test)
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cm = confusion_matrix(y_test, knn_predictions)
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print("KNN:\n", cm)
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# training a Naive Bayes classifier
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from sklearn.naive_bayes import GaussianNB
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gnb = GaussianNB().fit(X_train, y_train)
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gnb_predictions = gnb.predict(X_test)
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# accuracy on X_test
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accuracy = gnb.score(X_test, y_test)
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print(accuracy)
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# creating a confusion matrix
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cm = confusion_matrix(y_test, gnb_predictions)
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print("NB:\n", cm)
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