What is R-Squared in Simple Terms?

If you've worked with linear regression, you've probably heard of R-squared (R^2). But what does it actually mean?

R-squared tells us how well our model explains the variation in the data. In other words, it shows how much of the changes in the target (dependent variable) can be predicted by the features (independent variables).

Here's the breakdown:

Total Sum of Squares variance (TSS): How much the data varies overall.

Residual/Unexplained Sum of Squares variance (RSS): What the model can't explain.

Explained Sum of Squares variance (ESS): What the model can explain.

The formula is:

R^2=1?(RSS/TSS)        

What it means:

R^2= 1 --> Perfect: Model explains everything.

R^2=0-->Bad: Model explains nothing.

R^2<0-->Worse than guessing the average!

In short, R-squared helps us see how well the model fits the data, but it's not the only thing to consider.

What do you think? How do you evaluate your models?

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