Lab 3a. Fundamentals of Machine Learning: K-Nearest Neighbor (KNN) скачать в хорошем качестве

Lab 3a. Fundamentals of Machine Learning: K-Nearest Neighbor (KNN)

Summary This video tutorial focuses on the fundamentals of machine learning, particularly on the K-Nearest Neighbors (KNN) algorithm. This simple yet powerful technique is used for both classification and regression tasks. You will begin by understanding the basic principles behind KNN, including how it classifies data points based on the majority label of the nearest neighbors in the feature space. The tutorial will walk you through the process of training a KNN model. You will learn how to prepare your dataset and select the correct number of neighbors (the "K" in KNN). Next, the tutorial will cover how to visualize the decision boundaries of the KNN model, which is crucial in understanding how the algorithm divides the feature space to assign class labels. You will plot these boundaries using Python and libraries like Matplotlib, gaining insight into how KNN generalizes and makes predictions based on the proximity of new data points to its neighbors. By the end of this tutorial, you will have a solid understanding of how KNN works, how to implement it with your own data, and how to interpret its decision boundaries for better decision-making in machine learning tasks. Whether you are working with a classification problem (land cover classification) or a regression problem (predicting house prices based on features), this tutorial provides the foundational knowledge to get you started. Additional Materials: 1. Python Script https://github.com/ck1972/Geospatial-... 2. Access courses at Ai. Geelabs https://aigeolabs.com/courses/ https://aigeolabs.com/sign-up/ 3. Buy 'Explainable Machine Learning for Geospatial Data Analysis: A Data-Centric Approach' book https://aigeolabs.com/books/explainab...