Скачать или смотреть ⚡ Classification Report Explained | Precision, Recall, F1-Score | Model Evaluation | ML Course 2025

⚡ Classification Report Explained | Precision, Recall, F1-Score | Model Evaluation | AI & ML Course 2025

🎯 In this video, we dive deep into the classification_report from Scikit-learn to evaluate model performance using Precision, Recall, F1-score, Support, and Accuracy.

📝 What You’ll Learn in This Video:
📌 Classification Report Essentials:
✔️ How to generate a classification report using classification_report()
✔️ Understand Precision, Recall, and F1-Score with real output
✔️ Learn what Support means in the context of class labels
✔️ How these metrics complement accuracy and why they're essential in imbalanced datasets
✔️ Use case: Heart Disease Prediction Model Evaluation

👤 Ideal For:
✅ AI/ML beginners exploring model evaluation
✅ Data science students analyzing classification performance
✅ Anyone building binary or multiclass classifiers
✅ Professionals preparing for ML technical interviews

🎁 FREE AI/ML Study Resources Included:
✅ Classification Metrics Cheat Sheet
✅ Python Code for Scikit-learn Classification Report
✅ Annotated Heart Disease ML Notebook

🔥 Claim Your FREE Resources:
👍 Like, Subscribe & Comment on the video 📩 Email: [email protected] to receive your free resources!

📌 Watch This Video If You Want To:
🚩 Clearly understand key evaluation metrics like precision and recall
🚩 Evaluate classification models beyond accuracy
🚩 Learn how to interpret Scikit-learn’s detailed output
🚩 Gain insights into real-world model performance analysis

📣 Subscribe to FutureMinds AI & ML for real-world ML projects, evaluation techniques, and expert tutorials!

#ClassificationReport #PrecisionRecall #F1Score #ModelEvaluation #ScikitLearn #PythonML #HeartDiseasePrediction #FutureMindsAI #MachineLearning2025 #AIMetrics