Coefficient of Determination
Tags: #machine learning #metricEquation
$$SS_{res} = \sum_{i} (y_{i} - f_{i})^{2} = \sum_{i} e_{i}^{2}, SS_{total} = \sum_{i} (y_{i} - \bar{y} )^{2}, y=\frac{1}{n} \sum^{n}_{i=1} y_{i}, R^{2} = 1 - \frac{SS_{res}}{SS_{total}}$$Latex Code
SS_{res} = \sum_{i} (y_{i} - f_{i})^{2} = \sum_{i} e_{i}^{2}, SS_{total} = \sum_{i} (y_{i} - \bar{y} )^{2}, y=\frac{1}{n} \sum^{n}_{i=1} y_{i}, R^{2} = 1 - \frac{SS_{res}}{SS_{total}}
Have Fun
Let's Vote for the Most Difficult Equation!
Introduction
$$f_{i}$$: prediction values of original observed value $$ y_{i} $$.
$$ SS_{res} $$: denotes the residual sum of squares, which is the sum of square of residual value $$e_{i}$$.
$$ SS_{tot} $$: denotes total sum of squares.
$$ R^{2} $$: Coefficient of Determination or R-squared, which measures how good the modeled values $$f_{i}$$ exactly match the observed values $$y_{i} $$ .
References
Wikipedia: Coefficient of determinationDiscussion
Comment to Make Wishes Come True
Leave your wishes (e.g. Passing Exams) in the comments and earn as many upvotes as possible to make your wishes come true