Machine Learning & Artificial Intelligence

Vector is a list of numbers. Vector is widely used in the machine learning area as it is a foundation for many ML methods, like supervised…

A vector is a data structure with at least two components, as opposed to a scalar, which has just one. For example, a vector can represent velocity, an idea that combines speed and direction: wind velocity = (50mph, 35 degrees North East). A scalar, on the other hand, can represent something with one value like temperature or height: 50 degrees Celsius, 180 centimeters. Therefore, we can represent two-dimensional vectors as arrows on an x-y graph, with the coordinates x and y each representing one of the vector’s values.

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Naive Bayes Algorithm is a classification method that uses Bayes Theory. It assumes the presence of a specific attribute in a class.

Sklearn Naive Bayes Classifier Python. Learn how to build & evaluate a Gaussian Naive Bayes Classifier using Python's Scikit-learn package.

The Naive Bayes classifier works on the principle of conditional probability. Understand where the Naive Bayes fits in the machine learning hierarchy. Read on!

Learn how Naïve Bayes classifiers uses principles of probability to perform classification tasks, like topic classification.

Naive Bayes is the most popular machine learning classification method. Understand Naive Bayes classifier with different applications and examples.

Naive Bayes methods are a set of supervised learning algorithms based on applying Bayes’ theorem with the “naive” assumption of conditional independence between every pair of features given the val...

What is a classifier?

Naive Bayes Classifier in Machine Learning with Machine Learning, Machine Learning Tutorial, Machine Learning Introduction, What is Machine Learning, Data Machine Learning, Applications of Machine Learning, Machine Learning vs Artificial Intelligence etc.

A Computer Science portal for geeks. It contains well written, well thought and well explained computer science and programming articles, quizzes and practice/competitive programming/company interview Questions.

The K-Nearest Neighbors (K-NN) algorithm is a popular Machine Learning algorithm used mostly for solving classification problems. In this article, you'll learn how the K-NN algorithm works with practical examples. We'll use diagrams, as well sample data to show how you can classify data using the K-NN algorithm. We'll

Delve into K-Nearest Neighbors (KNN) classification with R. Learn how to use 'class' and 'caret' R packages, tune hyperparameters, and evaluate model performance.

This article covers how and when to use k-nearest neighbors classification with scikit-learn. Focusing on concepts, workflow, and examples. We also cover distance metrics and how to select the best value for k using cross-validation.

A complete explanation of K-NN

K-nearest neighbor is a supervised machine learning algorithm, also known as a lazy algorithm, used for classification and regression problems. Learn more.

K-Nearest Neighbours is one of the most basic yet essential classification algorithms in Machine Learning. It belongs to the supervised learning domain

Get an in-depth understanding of KNN Algorithm with this comprehensive guide, including how it works, when to use it, and how to implement it

K-Nearest Neighbor(KNN) Algorithm for Machine Learning with Machine Learning, Machine Learning Tutorial, Machine Learning Introduction, What is Machine Learning, Data Machine Learning, Applications of Machine Learning, Machine Learning vs Artificial Intelligence etc.

The k-nearest neighbors (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve both…

Learn more about one of the most popular and simplest classification and regression classifiers used in machine learning, the k-nearest neighbors algorithm.

Multiclass classification is one of the two types of classification algorithms in machine learning. Find out how DataRobot automates multiclass problems.

Let's have a look at what Supervised Learning is before moving on to Classification. Assume you're attempting to learn a new arithmetic idea.

Multiclass Classification in Machine Learning: We have heard of classification and regression techniques. If we dig deeper, we deal with two variables- binary & multiclass classification.

A Computer Science portal for geeks. It contains well written, well thought and well explained computer science and programming articles, quizzes and practice/competitive programming/company interview Questions.

This section of the user guide covers functionality related to multi-learning problems, including multiclass, multilabel, and multioutput classification and regression. The modules in this section ...

Classification problems having multiple classes with imbalanced dataset present a different challenge than a binary classification problem…