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Here’s how to interpret the output:

Precision: Out of all the players that the model predicted would get drafted, only 43% actually did.

Recall: Out of all the players that actually did get drafted, the model only predicted this outcome correctly for 36% of those players.

F1 Score: This value is calculated as:

• F1 Score: 2 * (Precision * Recall) / (Precision + Recall)
• F1 Score: 2 * (.43 * .36) / (.43 + .36)
• F1 Score: 0.40.

Since this value isn’t very close to 1, it tells us that the model does a poor job of predicting whether or not players will get drafted.

Support: These values simply tell us how many players belonged to each class in the test dataset. We can see that among the players in the test dataset, 160 did not get drafted and 140 did get drafted.

Note: You can find the complete documentation for the classification_report() function here.