Multi-class logistic regression is a moderately complex technique for multi-class classification problems. The main alternative is to use a neural network classifier with a single hidden layer. A ...

Generating synthetic data for both single-class and multi-class classification. Handling imbalanced data by using appropriate techniques (e.g., class weighting, sampling). Solving these problems using ...

Classification techniques are an essential part of machine learning and data mining applications. Approximately 70% of problems in Data Science are classification problems. There are lots of ...

Discover the limitations and challenges of logistic regression, and how to overcome them. Learn what are the advantages of using logistic regression for classification, and how to apply it to your ...

Logistic regression is a statistical technique used for binary classification ... than 0.5 as the positive class ("spam", in this case). For scenarios involving multiple categories, as in ...

this paper introduces fixed memory step gradient descent method into the optimization part of logistic regression algorithm, and combines OVR strategy to solve the problem of multi-class ...

Abstract: To improve the effect of logistic regression in multiobjective classification and explore its greatest potential, a set of training and classification algorithms is constructed, by using the ...

the classification report for the logistic regression model implemented is shown below. Image classification is one such application in the domain of Deep Learning and Image Processing where at ...

Machine learning models analyze complex patterns within medical datasets, enabling precise prediction and classification of diseases like CHD [14]-[18]. This study explores the application of three ...

Basic logistic regression can be used for binary classification, for example predicting if a person is male or female based on predictors such as age, height, annual income, and so on. Multi-class ...