This code performs classification using three different classification algorithms, namely Naive Bayes, Decision Tree, and K-Nearest Neighbors (KNN), on a red wine dataset. Here are the explanations of ...

This repository contains implementations of various classification algorithms using both traditional machine learning (ML) and deep learning (DL) techniques. Each model is implemented in Python using ...

The weighted k-NN classification algorithm has received increased attention recently for two reasons. First, by using neural autoencoding, k-NN can deal with mixed numeric and non-numeric predictor ...

Analyzing the quantity, type, and quality of your data and identifying your problem type (e.g., Classification) are the first steps towards choosing the best algorithms from Python ML modules.

Decision Trees: A decision tree is built by repeatedly asking questions about the partition data. A decision tree falls under supervised Machine Learning Algorithms in Python and comes of use for both ...

Machine learning algorithms in Python are considered the spine of present-day artificial intelligence because they go beyond classical programming techniques and are able to perform innovative and ...

Classification algorithms are widely used in data science to predict the category or label of a given data point based on its features. However, not all classification algorithms perform equally ...

Scikits are Python-based scientific toolboxes built around SciPy, the Python library for scientific computing. Scikit-learn is an open source project focused on machine learning: classification ...