Machine learning algorithms for predicting or categorizing data include classification and regression techniques. Regression algorithms are used to forecast a continuous numerical value, such as the ...

Abstract: This article proposes an algorithm for solving multivariate regression and classification problems using piecewise linear predictors over a polyhedral partition of the feature space. The ...

Application: support vector machines regression algorithms has found several applications in the oil and gas industry, classification of images and text and hypertext categorization. In the oilfields, ...

The most popular and commonly used algorithms include Random Forest (Classification), K-means (Clustering), Gradient Boosting (Regression), Apriori (Association Rule Mining), Principal Component ...

Which kind of algorithm works best (supervised, unsupervised, classification, regression, etc.) depends on the kind of problem you’re solving, the computing resources available, and the nature ...

Logistic regression. Classification algorithms can find solutions to supervised learning problems that ask for a choice (or determination of probability) between two or more classes.

Using different classification algorithms, Logistic regression, Neural Network, SVM, Random Forest) on MNIST data for predicting digits. Problem Statement-: The problem is about recognizing the 28 X ...

This project compares the performance of five classification algorithms (Logistic Regression, Decision Tree, Random Forest, SVM, and k-NN) on the Breast Cancer dataset from sklearn. It includes data ...

To evaluate the diagnostic accuracy of an algorithm, it can be compared to the best existing classification for the used dataset, for which the value 100 percent is assigned.