Machine Learning ... the relationship between inputs and outputs. This learning process involves the model generalizing patterns from the provided examples to make predictions on new, unseen data.

Logistic Regression is a widely used model in Machine Learning. It is used in binary classification, where output variable ...

Which kind of algorithm works best (supervised, unsupervised, classification, regression ... Managing machine learning models in production is, however, a whole other can of worms.

Based on the results, we can conclude combining classification and regression can improve the performance of visit based and customer based models with skewed data. We would like to thank the ...

Some machine learning models ... Generative models are often used to predict what occurs next in a sequence. Meanwhile, discriminative models are used for either classification or regression and they ...

Abstract: The research aims to attack a Logistic Regression-based Machine Learning Model using the Evasion and Poison technique. An adversarial attack is a strategy to fool machine learning models ...

Abstract: Multivariate ridge regression (MR), linear discriminant analysis (LDA) and extreme learning machine (ELM ... is chosen to improve classification accuracy. The tensor discriminant ridge ...