What is a R-squared?
A regression metric indicating how much variation in a label is due to an individual feature or to a feature set.
R-squared explained in plain English
A regression metric indicating how much variation in a label is due to an individual feature or to a feature set. R-squared is a value between 0 and 1, which you can interpret as follows: - An R-squared of 0 means that none of a label's variation is due to the feature set. - An R-squared of 1 means that all of a label's variation is due to the feature set. - An R-squared between 0 and 1 indicates the extent to which the label's variation can be predicted from a particular feature or the feature set. For example, an R-squared of 0.10 means that 10 percent of the variance in the label is due to the feature set, an R-squared of 0.20 means that 20 percent is due to the feature set, and so on. R-squared is the square of the Pearson correlation coefficient between the values that a model predicted and ground truth.
Example
Practitioners refer to r-squared when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.
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