Скачать или смотреть Model Evaluation Made Easy: Accuracy, Precision, Recall, F1 & AUC Explained!

How do you know if your machine learning model is actually good? 🤔
In this video, we’ll break down model evaluation metrics in the simplest way possible — perfect for beginners in Data Science, AI, and Machine Learning.

You’ll learn:
✅ What Accuracy, Precision, Recall, and F1-score really mean
✅ How to interpret the Confusion Matrix (TP, FP, FN, TN)
✅ The intuition behind Precision vs Recall trade-off
✅ What the ROC Curve and AUC Score tell us about model performance
✅ Real examples and formulas to understand these metrics step-by-step

By the end, you’ll confidently evaluate any classification model — from logistic regression to neural networks!

📊 Perfect for:

Data Science & AI students

Aspiring Machine Learning engineers

Anyone struggling to understand model performance metrics

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