Examining the model performance

We'll start by making predictions on our test data and then we'll examine whether our predictions were correct:

y_hat = clf.predict(X_test) 
y_true = y_test 
 
pdf = pd.DataFrame({'y_true': y_true, 'y_hat': y_hat}) 
 
pdf['correct'] = pdf.apply(lambda x: 1 if x['y_true'] == x['y_hat'] else 0, axis=1) 
 
pdf 

The preceding code generates the following output:

Let's now look at what percentage of the 200 IPOs in our test dataset we should have invested in—remember, that means they rose over 2.5% from the open to the close:

pdf['y_true'].value_counts(normalize=True) 

The preceding code generates the following output:

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