What is K Nearest Neighbors (KNN)?
K-nearest neighbors (KNN) is a machine learning algorithm categorized as lazy learning, employed in both classification and regression tasks. It works by assigning a data point to the class most common among its k nearest neighbors in the feature space. KNN is simple to understand and implement, making it a popular choice for beginners and for tasks where interpretability is important. However, it can be computationally expensive, especially with large datasets, as it requires storing all training data and computing distances for each prediction.
Logistic Regression vs K Nearest Neighbors in Machine Learning
Machine learning algorithms play a crucial role in training the data and decision-making processes. Logistic Regression and K Nearest Neighbors (KNN) are two popular algorithms in machine learning used for classification tasks. In this article, we’ll delve into the concepts of Logistic Regression and KNN and understand their functions and their differences.