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End to End ML Project - Fashion MNIST - Selecting the Model - Cross-Validation - The Conclusion

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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