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).
Taking you to the next exercise in seconds...
Want to create exercises like this yourself? Click here.
No hints are availble for this assesment
Answer is not availble for this assesment
Loading comments...