⚡ Classification Report Explained | Precision, Recall, F1-Score | Model Evaluation | ML Course 2025 скачать в хорошем качестве

⚡ 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