for i in C: for j in penalty: clf = LogisticRegression(C=i, penalty=j, solver='saga', max_iter=100).fit(X_train, y_train) scoring = [] accuracyScore = [] for train_index, test_index in tscv.split(X): print("TRAIN:", train_index, "TEST:", test_index) X_train, X_test = X[train_index], X[test_index] y_train, y_test = y[train_index], y[test_index] clf.fit(X_train, y_train) test_predictions = clf.predict_proba(X_test).T[1] test_score = clf.score(X_test, y_test) testAcScore = clf.accuracy_score(X_test, y_test) scoring.append(test_score) accuracyScore.append(testAcScore) #prediction = clf.predict() print(test_score) print('i: ', i) print('j: ', j) plt.plot(scoring) plt.plot(accuracyScore) plt.show() ''' clf = linear_model.LogisticRegression(C=1e5) clf.fit(X, y)
plot_feature_importance(forest.feature_importances_, X.columns, 'RANDOM FOREST CLASSIFIER') # In[12]: #logistic Regression from sklearn.linear_model import LogisticRegression logistic_regression = LogisticRegression() logistic_regression.fit(X_train, y_train) # In[14]: y_pred = logistic_regression.predict(X_test) accuracy = logistic_regression.accuracy_score(y_test, y_pred) accuracy_percentage = 100 * accuracy accuracy_percentage # In[ ]: from sklearn.linear_model import LogisticRegressionCV import numpy as np import matplotlib.pyplot as plt # get importance importance = logistic_regression.coef_[0] # summarize feature importance for i, v in enumerate(importance): print('Feature: %0d, Score: %.5f' % (i, v)) # plot feature importance