Скачать или смотреть Machine Learning Class 7 | Accuracy, Precision, Recall & R² Score Explained | Sir Nasir Hussain

📅 Date: 9 October 2025
🎓 Class: 7
📚 Topic: Accuracy, Precision, Recall & R² Score in Machine Learning
👨‍🏫 Instructor: Sir Nasir Hussain
🏫 Institute: Saylani Z.A.I.T Park — AI & Data Science Course

💡 Class Overview

Welcome to Machine Learning Class 7 of the AI & Data Science Course taught by Sir Nasir Hussain at Saylani Z.A.I.T Park.
In this class, we focus on one of the most crucial parts of any Machine Learning workflow — Model Evaluation Metrics.

When we build a model, it’s not enough to just train it. We must understand how well it performs, how accurate it is, and where it makes mistakes. That’s exactly what we’ll learn in today’s session.

🔍 What You’ll Learn in This Class

In this detailed and practical lecture, you’ll explore:

What are Evaluation Metrics in Machine Learning

Understanding Accuracy Score — how correct our model’s predictions are

Precision — how many of our positive predictions are actually correct

Recall (Sensitivity) — how many of the actual positives we captured

F1 Score — balancing Precision and Recall

R² Score (Coefficient of Determination) — measuring regression model performance

Using sklearn.metrics library for implementation

Confusion Matrix Visualization in Python using Matplotlib

Real-world project examples with Classification and Regression models

💻 Hands-on Python Practice

We’ll use Python, Scikit-learn, and Matplotlib to calculate and visualize these metrics.
You’ll learn to:

Import and use accuracy_score, precision_score, recall_score, and r2_score

Compare multiple models

Analyze model efficiency and overfitting issues

Choose the best model for your dataset

All code examples will be explained line-by-line for easy understanding.

🧩 Real-World Example

In this session, we’ll use a sample dataset (like predicting exam results or spam detection) and evaluate the model performance using these metrics.
You’ll clearly understand the difference between good and poor model predictions.

🚀 Why You Should Watch

By the end of this class, you’ll be able to:

Measure the accuracy and effectiveness of ML models

Visualize performance using confusion matrix

Use multiple evaluation metrics confidently

Build more reliable and professional Machine Learning projects

📚 Previous & Upcoming Classes

📘 Previous Class (Class 6): Classification Algorithms (Decision Tree, Random Forest, KNN, SVM)
📘 Next Class: Model Improvement Techniques and Hyperparameter Tuning

💬 Instructor Message

“In Machine Learning, understanding model evaluation is more important than just building the model. Without metrics, your ML model is like a car without a dashboard.”
— Sir Nasir Hussain

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