an RDD of (prediction, observation) pairs
True if the regression is through the origin. For example, in linear regression, it will be true without fitting intercept.
Returns the variance explained by regression.
Returns the variance explained by regression. explainedVariance = $\sum_i (\hat{y_i} - \bar{y})2 / n$
Returns the mean absolute error, which is a risk function corresponding to the expected value of the absolute error loss or l1-norm loss.
Returns the mean absolute error, which is a risk function corresponding to the expected value of the absolute error loss or l1-norm loss.
Returns the mean squared error, which is a risk function corresponding to the expected value of the squared error loss or quadratic loss.
Returns the mean squared error, which is a risk function corresponding to the expected value of the squared error loss or quadratic loss.
Returns R2, the unadjusted coefficient of determination.
Returns R2, the unadjusted coefficient of determination.
J. G. Eisenhauer, Regression through the Origin. Teaching Statistics 25, 76-80 (2003)
Coefficient of determination (Wikipedia) In case of regression through the origin, the definition of R2 is to be modified.
Returns the root mean squared error, which is defined as the square root of the mean squared error.
Returns the root mean squared error, which is defined as the square root of the mean squared error.
Evaluator for regression.