In this programming assignment we implement Logistic Regression using binary logistic regression classifier and multi-class logistic regression classifier to classify the handwritten digits. We ...

Another difference between binary and multi-class classification models is how you measure their performance. For binary classification, you can use metrics such as accuracy, precision, recall, F1 ...

This repository contains two Python scripts for classifying the Iris dataset using different machine learning approaches: a multi-class classifier with K-Nearest Neighbors (KNN) and a binary ...

LightGBM can perform binary classification , multi-class classification, (predict one of three or more possible values), regression (predict a single numeric value) and ranking. [Click on ... The demo ...

Arguably the most powerful multi-class classification technique is a neural network model. There are several tools and code libraries that you can use to create a neural network classifier. The scikit ...

Binary decomposition of a multi-class classification problem is a widely used method in the field of machine learning, which involves using an ensemble of binary classifiers to undertake multi-class ...

These methods facilitate the integration of varied data types for predictive tasks like regression, binary classification, and multi-class classification. For instance, whether predicting age based on ...