new RegressionMetrics(predictionAndObservations, predictionAndObservations)
Evaluator for regression.
Parameters:
| Name | Type | Description |
|---|---|---|
predictionAndObservations |
an RDD of (prediction, observation) pairs. | |
predictionAndObservations |
module:eclairjs/rdd.RDD |
Methods
explainedVariance() → {Promise.<Number>}
Returns the variance explained by regression.
explainedVariance = \sum_i (\hat{y_i} - \bar{y})^2 / n
- Source:
- See:
-
- [[https://en.wikipedia.org/wiki/Fraction_of_variance_unexplained]]
Returns:
A Promise that resolves to the variance explained by regression.
- Type
- Promise.<Number>
meanAbsoluteError() → {Promise.<Number>}
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:
A Promise that resolves to the absolute mean.
- Type
- Promise.<Number>
meanSquaredError() → {Promise.<Number>}
Returns the mean squared error, which is a risk function corresponding to the
expected value of the squared error loss or quadratic loss.
Returns:
A Promise that resolves to the mean squared error.
- Type
- Promise.<Number>
r2() → {Promise.<Number>}
Returns R^2^, the unadjusted coefficient of determination.
- Source:
- See:
-
- [[http://en.wikipedia.org/wiki/Coefficient_of_determination]] In case of regression through the origin, the definition of R^2^ is to be modified.
- J. G. Eisenhauer, Regression through the Origin. Teaching Statistics 25, 76-80 (2003) [[https://online.stat.psu.edu/~ajw13/stat501/SpecialTopics/Reg_thru_origin.pdf]]
Returns:
A Promise that resolves to the result.
- Type
- Promise.<Number>
rootMeanSquaredError() → {Promise.<Number>}
Returns the root mean squared error, which is defined as the square root of
the mean squared error.
Returns:
A Promise that resolves to the root mean squared error.
- Type
- Promise.<Number>