<jupyter_start><jupyter_text>save model<jupyter_code>#for Logistic Regression Classifier

#Calculating False Positive Rate,True Positive Rate and threshold
fpr_logreg, tpr_logreg, _ = roc_curve(y_test, preds_logreg)
roc_auc_logreg = auc(fpr_logreg, tpr_logreg)

#import matplotlib.pyplot as plt
plt.title('Receiver Operating Characteristic for Logistic Regression')
plt.plot(fpr_logreg, tpr_logreg, 'b', label = 'AUC = %0.2f' % roc_auc_logreg)
plt.legend(loc = 'lower right')
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()<jupyter_output><empty_output>
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{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '644369', 'license': 'MIT', 'n_tokens_mistral': 283, 'n_tokens_neox': 265, 'n_words': 55, 'path': 'Logestic_Regression/main_cv.ipynb', 'repo_name': 'arun-praj/Sentiment-Analysis-from-scratch', 'size': '663'}