Summary
This chapter covers maintaining class balance in datasets to prevent model bias by ensuring equitable representation of all target classes during training.

General domain of usage
Fraud detection

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.

In this project, our primary objective will be to delve into the identification of handwritten digits through the application of machine learning algorithms. This endeavor aims to harness the power of machine learning to effectively interpret and understand handwritten digits, showcasing the potential of these algorithms in processing and analyzing complex visual information.

In this project, our primary objective will be to delve into the identification of handwritten digits through the application of machine learning algorithms.

Recognizing Handwritten Digits

Class Balance

Soluzione