Evaluation Metrics for Machine Learning Models | Full Course скачать в хорошем качестве

Evaluation Metrics for Machine Learning Models | Full Course

Welcome to my latest video where we'll be sharing with you the essential concepts of evaluation metrics for classification and regression in machine learning. In this video, you'll learn how to assess the performance of your machine-learning models and make them better.Whether you're working on a classification problem, where you need to predict the class of an object, or a regression problem, where you need to predict a continuous variable, evaluation metrics are crucial for understanding the strengths and weaknesses of your model. In this tutorial, we'll cover the most common evaluation metrics, including accuracy, precision, recall, F1-score, and R-squared. We'll explain what they mean, how to calculate them, and what they reveal about your model's performance.Moreover, we'll provide practical examples and walk you through the process of selecting the most appropriate evaluation metric for your particular problem. By the end of this video, you'll have a solid understanding of how to use evaluation metrics to optimize your machine-learning models and improve their accuracy and precision.So, whether you're a beginner or an experienced data scientist, make sure to watch this video to take your machine-learning skills to the next level. Don't forget to subscribe to our channel for more exciting tutorials on data science and machine learning. Stay on top of your industry by interacting with us on our social channels: Follow us on Instagram:   / analytics_vidhya   Like us on Facebook:   / analyticsvidhya   Follow us on Twitter:   / analyticsvidhya   Follow us on LinkedIn:  / analytics-vidhya