Avoid these common mistakes when evaluating a model's performance in Python

There are some common mistakes that individuals face when evaluating a model’s performance in Python, some most frequently occurring mistakes are explained below with the help of an example code.

1. Ignoring class imbalance:

Class imbalance is a phenomenon when one class occurs more frequently than the others in the dataset and can cause biased results which are misleading. To deal with this, one can use hyperparameters to set different weights (importance) for each class.

2. Focusing solely on accuracy:

Many individuals rely solely on the accuracy metric, which is not sufficient in many cases. It is crucial to calculate and consider multiple metrics together to make a comprehensive evaluation of a model’s performance. In the following sample code, various commonly used metrics such as accuracy score, F1 score, recall score, precision score, and confusion matrix are computed.

3. Not using cross-validation for robust evaluation:

A common mistake is to evaluate the model’s performance on a single train-test split without considering the potential variation in performance across different data subsets. This can lead to an overly optimistic or pessimistic view of the model’s performance.

To address this, cross-validation techniques such as k-fold cross-validation can be employed to evaluate the model’s performance across multiple train-test splits and provide a more robust assessment.