Dealing with Mislabeled Datasets Using Pretrained Models

In this lesson, we’ll learn how to identify and remove mislabeled instances from a dataset using a pretrained model—a model that is trained on a large and diverse dataset before being applied to a specific task or problem.

Mislabeled data can significantly affect the performance and reliability of ML models. It’s important to understand how we can effectively remove or correct mislabeled instances in order to maintain data quality and enhance model performance.

Identifying and removing mislabeled instances using a pretrained model

To identify and remove mislabeled instances using a pretrained model, we use two different datasets. First, we use a clean dataset to train our ML model. Once trained, we use this pretrained model on a new dataset (not yet seen by the model) to identify and remove mislabeled instances in that new dataset. In the following steps, we’ll break down the pretraining process.

Step 1: Importing libraries

The following code imports the necessary libraries for the implementation of identifying and removing mislabeled instances from the dataset:

Step 2: Loading and creating an unbiased mislabeled dataset

The code provided below loads the MNIST digit dataset using the Keras library. We assume that the dataset is clean, which means the labels ...