Contribute to Srushanth/Binary-Classification-with-a-Software-Defects-Dataset development by creating an account on GitHub. ... train.csv - the training dataset; defects is the binary target, which is ...

This project implements a simple binary classification neural network to distinguish between the digits 0 and 1 from the MNIST dataset. The model achieves excellent accuracy and low loss on both the ...

For most alternative binary classifier architectures, you would have to use a Dataset definition that more closely resembles one designed for multi-class classification than for binary classification.

Learn how to deal with class imbalance in binary classification datasets using resampling, weighting, and thresholding methods. Find out how to choose and evaluate the best method for your data ...

The goal of a binary classification problem is to predict an output value that can be one of just two possible discrete values, such as "male" or "female." This article is the third in a series of ...

The binary classification problem is a fundamental and core problem type in machine learning, and many machine learning algorithms, such as logistic regression and tree models, are widely used to ...

When working on imbalanced medical datasets for a classification task, the chosen method depends mainly on label availability. We consider two classes, e.g., normal and abnormal. If the dataset is ...

Learn six techniques to deal with class imbalance in binary classification tasks, and improve your machine learning algorithm's accuracy and performance.