Testing the baseline model
In this section, we will implement the code, which will give us an idea about how good or how bad our trained ML models perform in a validation set. We are using the mean accuracy score and the AUC-ROC score.
Here, we have generated five different classifiers and, after performing testing for each of them on the validation dataset, which is 25% of held-out dataset from the training dataset, we will find out which ML model works well and gives us a reasonable baseline score. So let's look at the code:.
Figure 1.55: Code snippet to obtain a test score for the trained ML model
In the preceding code snippet, you can see the scores ...
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