Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.
Class Balance | Recognizing Handwritten Digits
Recognizing Handwritten Digits
course content

Course Content

Recognizing Handwritten Digits

Class Balance

Ensuring class balance in machine learning datasets is crucial for avoiding model bias towards any specific class. When a dataset is balanced, it signifies an equitable representation of all classes, which is vital. An imbalanced dataset can result in suboptimal performance of the model, especially in predicting the minority class.

Consider a dataset comprising customer transactions where 90% are legitimate and only 10% fraudulent. Training a model on such skewed data might incline it to predict most transactions as legitimate. This inclination occurs because the model is tailored to reduce overall error, and identifying most transactions as legitimate boosts accuracy, albeit superficially.

Hence, maintaining class balance is imperative for training models on a diverse sample from each class, enhancing their ability to make precise predictions across the board.

Task

  1. Extract the target labels (digit values) from the MNIST dataset and assign them to variable Y.
  2. Visualize the frequency distribution of digit labels from the MNIST dataset by creating a count plot.

Task

  1. Extract the target labels (digit values) from the MNIST dataset and assign them to variable Y.
  2. Visualize the frequency distribution of digit labels from the MNIST dataset by creating a count plot.

Mark tasks as Completed
Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Ensuring class balance in machine learning datasets is crucial for avoiding model bias towards any specific class. When a dataset is balanced, it signifies an equitable representation of all classes, which is vital. An imbalanced dataset can result in suboptimal performance of the model, especially in predicting the minority class.

Consider a dataset comprising customer transactions where 90% are legitimate and only 10% fraudulent. Training a model on such skewed data might incline it to predict most transactions as legitimate. This inclination occurs because the model is tailored to reduce overall error, and identifying most transactions as legitimate boosts accuracy, albeit superficially.

Hence, maintaining class balance is imperative for training models on a diverse sample from each class, enhancing their ability to make precise predictions across the board.

Task

  1. Extract the target labels (digit values) from the MNIST dataset and assign them to variable Y.
  2. Visualize the frequency distribution of digit labels from the MNIST dataset by creating a count plot.

Mark tasks as Completed
Switch to desktop for real-world practiceContinue from where you are using one of the options below
Section 1. Chapter 4
AVAILABLE TO ULTIMATE ONLY
We're sorry to hear that something went wrong. What happened?
some-alt