In this lesson, we’ll explore what happens when we introduce a small amount of biased mislabeling in the dataset. Our primary goal is to gain a comprehensive understanding of the performance degradation that can arise when dealing with low-quality data. To understand the effect, we’ll use the CNN model with two versions of the dataset—one with a clean dataset and the other with a mislabeled dataset. We’ll then compare the performance using accuracy matrices, which will help us gauge the impact of adding a small amount of biased mislabeling to our dataset.

Implementing biased mislabeling

To evaluate how a dataset’s quality affects a CNN model’s performance, we’ll follow a series of steps to compare the respective performance achieved using a clean and mislabeled dataset.

Step 1: Importing libraries

The following code imports the necessary libraries for implementing unbiased mislabeling:

Get hands-on with 1200+ tech skills courses.