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Decision Tree Linear Classfiers

Ao Ouyang answered on October 6, 2022 Popularity 3/10 Helpfulness 1/10

answer Decision Tree Linear Classfiers

Decision Tree Linear Classfiers

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from sklearn import linear_model
from sklearn.svm import SVC

C = [0.01, 0.1, 0.2, 0.5, 0.8, 1, 5, 10, 20, 50]
LRtrainAcc = []
LRtestAcc = []
SVMtrainAcc = []
SVMtestAcc = []

for param in C:
    clf = linear_model.LogisticRegression(C=param)
    clf.fit(X_train, Y_train)
    Y_predTrain = clf.predict(X_train)
    Y_predTest = clf.predict(X_test)
    LRtrainAcc.append(accuracy_score(Y_train, Y_predTrain))
    LRtestAcc.append(accuracy_score(Y_test, Y_predTest))

    clf = SVC(C=param,kernel='linear')
    clf.fit(X_train, Y_train)
    Y_predTrain = clf.predict(X_train)
    Y_predTest = clf.predict(X_test)
    SVMtrainAcc.append(accuracy_score(Y_train, Y_predTrain))
    SVMtestAcc.append(accuracy_score(Y_test, Y_predTest))

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12,6))
ax1.plot(C, LRtrainAcc, 'ro-', C, LRtestAcc,'bv--')
ax1.legend(['Training Accuracy','Test Accuracy'])
ax1.set_xlabel('C')
ax1.set_xscale('log')
ax1.set_ylabel('Accuracy')

ax2.plot(C, SVMtrainAcc, 'ro-', C, SVMtestAcc,'bv--')
ax2.legend(['Training Accuracy','Test Accuracy'])
ax2.set_xlabel('C')
ax2.set_xscale('log')
ax2.set_ylabel('Accuracy')

Popularity 3/10 Helpfulness 1/10 Language whatever

Source: www.cse.msu.edu

Tags: decision-tree whatever

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Contributed on Oct 06 2022

Ao Ouyang

0 Answers Avg Quality 2/10

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