Day 11 | Linear Regression Theory Complete Guide| Assumptions, Correlation, MSE, MAE, RMSE, R2 Score
Автор: DataLearnm
Загружено: 2026-02-18
Просмотров: 43
Описание:
In this video, we cover Linear Regression theory concepts in detail, including important assumptions and evaluation metrics used in Machine Learning.
📚 Topics Covered:
🔹 What is Linear Regression?
🔹 Assumptions of Linear Regression
• Linearity
• Independence
• Homoscedasticity
• Normality
• No Multicollinearity
🔹 Coefficient of Correlation (r)
🔹 Mean Squared Error (MSE)
🔹 Mean Absolute Error (MAE)
🔹 Root Mean Squared Error (RMSE)
🔹 R² Score (Coefficient of Determination)
🔹 Adjusted R² Score
We also explain the mathematical intuition behind each metric and when to use them in real-world Machine Learning projects.
This video is perfect for:
✔️ BCA / BSc / MCA / Engineering Students
✔️ Data Science & Machine Learning Beginners
✔️ Interview Preparation
✔️ Competitive Exams
📌 By the end of this video, you will clearly understand how Linear Regression works and how to evaluate model performance using different error metrics.
If you found this video helpful, don’t forget to Like 👍, Share 🔁, and Subscribe 🔔 for more Machine Learning tutorials.
#MachineLearning #LinearRegression #MSE #RMSE #MAE #R2Score #DataScience #Statistics #MLTheory
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