Simulating Biased Mislabeling Using Python Programming
Learn how to simulate biased mislabeling in the MNIST digit dataset using Python programming.
The primary focus of this lesson is to simulate noise in a dataset and demonstrate its impact through visualization. This lesson offers hands-on learning experience simulating biased mislabeling in the MNIST digit dataset using Python programming. The lesson is divided into the following two steps:
Step 1: We will simulate biased mislabeling by manipulating the labels based on predefined biases or assumptions. We’ll learn to modify labels to create mislabeling based on similar features between different classes. Moreover, we’ll actively simulate noise in the dataset through the provided code examples and instructions.
Step 2: We will visualize the dataset after simulating biased mislabeling. We’ll also generate a bar chart to observe the distribution of mislabeled images across different digits. This visualization will ...
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