When to Use Linear Regression? Advantages & Limitations Explained

Описание: 📊 Linear Regression is one of the most popular algorithms in statistics and machine learning — but it’s not always the best choice.

In this video, we’ll explore the advantages and disadvantages of using Linear Regression so you can understand when it's effective and when it might fail.

✅ What You'll Learn:
✔ Advantages of Linear Regression

Easy to understand and implement

Computationally efficient

Requires little tuning

Works well with linearly related data

Coefficients are interpretable

❌ Disadvantages of Linear Regression

Assumes linearity between input and output

Sensitive to outliers

Can underperform with complex or nonlinear data

Not suitable when predictors are highly correlated (multicollinearity)

Poor with categorical and non-numeric data without preprocessing

🎯 Ideal For:
Beginner data scientists, machine learning students, and anyone looking to understand regression models more deeply.

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