with open(path, 'rb') as file: unbalanced_model = pickle.load(file) # unpack balanced model path = '1) classification algorithms/random forest/credit card fraud/model_forest_balanced_ori.pkl' with open(path, 'rb') as file: balanced_model = pickle.load(file) # predict labels unbalanced_predictions = unbalanced_model.predict(X_test_unbalanced) unbalanced_predictions = [int(round(x)) for x in unbalanced_predictions] balanced_predictions = balanced_model.predict(X_test_balanced) balanced_predictions = [int(round(x)) for x in balanced_predictions] # print the confusion matrix, precision, recall, etc. get_model_performance(unbalanced_model, 'unbalanced', X_test_unbalanced, y_test_unbalanced, 'RF', 'fraud dataset') plt.savefig( '1) classification algorithms/assess model performance/credit card fraud/figures/PRcurve_rf_unbalanced_fraud.png' ) plt.close() get_model_performance(balanced_model, 'balanced', X_test_balanced, y_test_balanced, 'RF', 'fraud dataset') plt.savefig( '1) classification algorithms/assess model performance/credit card fraud/figures/PRcurve_rf_balanced_fraud.png' ) plt.close() cm_analysis( y_test_balanced, balanced_predictions, filename=
# unpack unbalanced model path = '1) classification algorithms/SVM/credit card fraud/model_SVM_balanced.pkl' with open(path, 'rb') as file: unbalanced_model = pickle.load(file) # unpack balanced model path = '1) classification algorithms/SVM/credit card fraud/model_SVM_balanced.pkl' with open(path, 'rb') as file: balanced_model = pickle.load(file) # predict labels unbalanced_predictions = unbalanced_model.predict(X_test_unbalanced) balanced_predictions = balanced_model.predict(X_test_balanced) # print the confusion matrix, precision, recall, etc. get_model_performance(unbalanced_model, 'unbalanced', X_test_unbalanced, y_test_unbalanced) get_model_performance(balanced_model, 'balanced', X_test_balanced, y_test_balanced) # plot confusion matrix graph plot_confusion_matrix(y_test_balanced, balanced_predictions, classes=['normal', 'fraud'], normalize=True, title='Confusion matrix balanced') plt.show() plot_confusion_matrix(y_test_unbalanced, unbalanced_predictions, classes=['normal', 'fraud'], normalize=True,
with open(path, 'rb') as file: unbalanced_model = pickle.load(file) # unpack balanced model path = '1) classification algorithms/random forest/bioresponse/model_forest_balanced_bio.pkl' with open(path, 'rb') as file: balanced_model = pickle.load(file) # predict labels unbalanced_predictions = unbalanced_model.predict(X_test_unbalanced) unbalanced_predictions = [int(round(x)) for x in unbalanced_predictions] balanced_predictions = balanced_model.predict(X_test_balanced) balanced_predictions = [int(round(x)) for x in balanced_predictions] # print the confusion matrix, precision, recall, etc. get_model_performance(unbalanced_model, 'unbalanced', X_test_unbalanced, y_test_unbalanced, 'RF', 'bioresponse dataset') plt.savefig( '1) classification algorithms/assess model performance/bioresponse/figures/PRcurve_rf_unbalanced_bio.png' ) plt.close() get_model_performance(balanced_model, 'balanced', X_test_balanced, y_test_balanced, 'RF', 'bioresponse dataset') plt.savefig( '1) classification algorithms/assess model performance/bioresponse/figures/PRcurve_rf_balanced_bio.png' ) plt.close() cm_analysis( y_test_balanced, balanced_predictions, filename=
path2 = '1) classification algorithms/neural networks/customer churn/model_NN_balanced_churn.pkl' # with open(path, 'rb') as file: # balanced_model = pickle.load(file) balanced_model = load_model(path2) # predict labels unbalanced_predictions = unbalanced_model.predict(X_test_unbalanced) unbalanced_predictions = unbalanced_predictions[:, 1] unbalanced_predictions = [int(round(x)) for x in unbalanced_predictions] balanced_predictions = balanced_model.predict(X_test_balanced) balanced_predictions = balanced_predictions[:, 1] balanced_predictions = [int(round(x)) for x in balanced_predictions] # print the confusion matrix, precision, recall, etc. get_model_performance(unbalanced_model, 'unbalanced', X_test_unbalanced, y_test_unbalanced, 'NN', 'churn dataset') plt.savefig( '1) classification algorithms/assess model performance/customer churn/figures/PRcurve_nn_unbalanced_churn.png' ) plt.close() get_model_performance(balanced_model, 'balanced', X_test_balanced, y_test_balanced, 'NN', 'churn dataset') plt.savefig( '1) classification algorithms/assess model performance/customer churn/figures/PRcurve_nn_balanced_churn.png' ) plt.close() cm_analysis( y_test_balanced, balanced_predictions, filename=