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  1. Learning
  2. Supervised Learning
  3. Regression
  4. Linear regression

The co-efficient of determination or r-squared

PreviousThe gradient descent techniqueNextComputing r-squared

Last updated 5 years ago

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R-squared is a statistical measure of how close the data is to the fitted regression line. R-squared is always between 0 and 100%. The higher the R-squared, the better the model fits your data.

The R-squared coefficient

The R-squared coefficient, also known as the coefficient of determination, is a measure of how well a model fits a dataset. It is commonly used in statistics. It measures the degree of variation in the target variable; this is explained by the variation in the input features. An R-squared coefficient generally takes a value between 0 and 1, where 1 equates to a perfect fit of the model.

how do I know how good my regression is? How well does my line fit my data? That's where r-squared comes in, and r-squared is also known as the coefficient of determination. Again, someone trying to sound smart might call it that, but usually it's called r-squared.

It is the fraction of the total variation in Y that is captured by your models. So how well does your line follow that variation that's happening? Are we getting an equal amount of variance on either side of your line or not? That's what r-squared is measuring.

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