Build machine learning models in Python using popular machine learning libraries NumPy & scikit-learn. Build & train supervised machine learning models for prediction & binary classification tasks, ...

Machine learning uses algorithms to turn a data set into a model that can identify patterns or make predictions from new data. Which algorithm works best depends on the problem.

Meanwhile, discriminative models are used for either classification or regression and they return a prediction based on conditional probability. Let’s explore the differences between generative and ...

This project aims to develop machine learning models for predicting final spinal curvature and anticipating curve progression in children with AIS. Project by: Harish Mohana Swami Naidu Javvadi Sai ...

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 ...

Logistic Regression is a widely used model in Machine Learning. It is used in binary classification, where output variable can only take binary values. Some real world examples where Logistic ...

This study evaluates the predictive performance of three machine learning models—Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM)—for classifying CHD. The models were ...

The classification model is used to predict whether or not a customer will generate revenue. If the revenue is predicted as non-zero, the sample will not enter our regression model. By doing this, the ...

People who are likely to get myopia can be classified using machine learning models. In this paper, myopic data set is classified using various supervised machine learning techniques like logistic ...