Скачать или смотреть When Should I Use Which Distance Metric In Unsupervised Learning? - The Friendly Statistician

When Should I Use Which Distance Metric In Unsupervised Learning? Have you ever wondered how to measure the similarity or difference between data points in your analysis? In this informative video, we'll explain the different distance metrics used in unsupervised learning. We'll start by discussing what distance metrics are and why choosing the right one is essential for your algorithms to perform effectively. You'll learn about common measures such as Euclidean distance, which works well with continuous data on similar scales, and when to normalize your features for better results. We’ll also cover Manhattan distance, ideal for high-dimensional data or when outliers are present, and Cosine distance, which is especially useful for text data and comparing document similarities based on orientation rather than magnitude. Additionally, we'll explore Mahalanobis distance, which considers the correlation between features and provides a more accurate measure when features are related, like height and weight. Whether you're working with numeric, categorical, or mixed data, understanding these metrics will help you select the best one for your clustering and dimensionality reduction tasks. We'll also share tips on testing different metrics to improve your model performance. Join us for this insightful discussion and subscribe to our channel for more helpful tips on data analysis and machine learning.

⬇️ Subscribe to our channel for more valuable insights.

🔗Subscribe: https://www.youtube.com/@TheFriendlyS...

#DataScience #MachineLearning #UnsupervisedLearning #DistanceMetrics #Clustering #DataAnalysis #DataVisualization #AI #DataMining #Statistics #DataScienceTips #DataMetrics #DataProcessing #MLAlgorithms #DataTech

About Us: Welcome to The Friendly Statistician, your go-to hub for all things measurement and data! Whether you're a budding data analyst, a seasoned statistician, or just curious about the world of numbers, our channel is designed to make statistics accessible and engaging for everyone.