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You can print the various metrics of each model the following way:
print("=== Softmax === ") display_scores(log_cv_scores) print("log_cv_accuracy:", log_cv_accuracy) print("log_cv_precision:", log_cv_precision) print("log_cv_recall:", log_cv_recall) print("log_cv_f1_score:", log_cv_f1_score) print("=== Random Forest === ") display_scores(rnd_cv_scores) print("rnd_cv_accuracy:", rnd_cv_accuracy) print("rnd_cv_precision:", rnd_cv_precision) print("rnd_cv_recall :", rnd_cv_recall ) print("rnd_cv_f1_score:", rnd_cv_f1_score)
From the results of the cross-validation process, we see that both the logistic regression and random forest have given the best results (nearly accuracy - 85%, standard deviation for accuracy - 0.002, Precision, Recall, F1 score nearly 0.85).
Let us use Voting Classifier and proceed with the fine-tuning of the model (hyperparameters tuning).
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