trait RegressionResult extends PredictionResult[Double]
Additional regression-specific interface
This interface is experimental and SHOULD BE REVIEWED before being merged into master.
In particular, an explanation of how the different methods relate to each other,
how predictive uncertainty is decomposed, and what the assumptions are
should be added, as these are currently not entirely clear.
For example, does the interface assume that the predictions are the mean of a predictive distribution (as opposed to, for example, the median, or the value with highest probability)? Does it assume the predictive distribution to be normal? Such assumptions are fine, but should be explicitly stated.
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- abstract def getExpected(): Seq[Double]
Get the expected values for this prediction
Get the expected values for this prediction
- returns
expected value of each prediction
- Definition Classes
- PredictionResult
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- def getBias(): Option[Seq[Double]]
**EXPERIMENTAL** Get the estimated bias of each prediction, if possible
**EXPERIMENTAL** Get the estimated bias of each prediction, if possible
The bias is signed and can be subtracted from the prediction to improve accuracy. See https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff
It is unclear if this method will be a stable member of the interface. It should be reviewed before the next formal release.
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- def getGradient(): Option[Seq[Vector[Double]]]
Get the gradient or sensitivity of each prediction
Get the gradient or sensitivity of each prediction
- returns
a vector of doubles for each prediction
- Definition Classes
- PredictionResult
- def getImportanceScores(): Option[Seq[Seq[Double]]]
Get the training row scores for each prediction
Get the training row scores for each prediction
- returns
sequence (over predictions) of sequence (over training rows) of importances
- Definition Classes
- PredictionResult
- def getInfluenceScores(actuals: Seq[Any]): Option[Seq[Seq[Double]]]
Get the improvement (positive) or damage (negative) due to each training row on a prediction
Get the improvement (positive) or damage (negative) due to each training row on a prediction
- actuals
to assess the improvement or damage against
- returns
Sequence (over predictions) of sequence (over training rows) of influence
- Definition Classes
- PredictionResult
- def getQuantile(quantile: Double, observational: Boolean = true): Option[Seq[Double]]
- def getQuantileMean(quantile: Double): Option[Seq[Double]]
Get a quantile from the distribution of predicted means, if possible
Get a quantile from the distribution of predicted means, if possible
The distribution for which these quantiles are computed should have zero-mean (i.e. no bias)
- quantile
to get, taken between 0.0 and 1.0 (i.e. not a percentile)
- def getStdDevMean(): Option[Seq[Double]]
Get the standard deviation of the distribution of predicted mean observations, if possible
Get the standard deviation of the distribution of predicted mean observations, if possible
The variation is due to the finite size of the training data, which can be thought of as being sampled from some training data distribution. This statistic is related to the variance in the bias-variance trade-off https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff
- def getStdDevObs(): Option[Seq[Double]]
Get the standard deviation of the distribution of predicted observations, if possible
Get the standard deviation of the distribution of predicted observations, if possible
Observations of the predicted variable are expected to have a stddev that matches this value. This statistic is related to the https://en.wikipedia.org/wiki/Prediction_interval It does not include estimated bias, even if the regression result contains a bias estimate.
- def getTotalError(): Option[Seq[Double]]
Get the expected error of the predicted mean observations, if possible
Get the expected error of the predicted mean observations, if possible
The mean of a large sample of repeated observations are expected to have a root mean squared error of the mean that matches this value. This statistic is related to the https://en.wikipedia.org/wiki/Confidence_interval This statistic includes the contribution of the estimated bias. E.g., for a normal distribution of predicted means, the total error is sqrt(bias**2 + variance)
- def getTotalErrorObs(): Option[Seq[Double]]
Get the expected error of the observations, if possible
Get the expected error of the observations, if possible
This statistic is related to the https://en.wikipedia.org/wiki/Prediction_interval This statistic includes the contribution of the estimated bias. E.g., for a normal distribution of predicted means, the total error is sqrt(bias**2 + variance).
- def getTotalErrorQuantile(quantile: Double): Option[Seq[Double]]
Get a quantile from the distribution of predicted means, if possible
Get a quantile from the distribution of predicted means, if possible
The distribution for which these quantiles are computed could be biased, e.g. if the bias is estimated but not corrected.
- quantile
to get, taken between 0.0 and 1.0 (i.e. not a percentile)
- def getTotalErrorQuantileObs(quantile: Double): Option[Seq[Double]]
Get a quantile from the distribution of predicted observations, if possible
Get a quantile from the distribution of predicted observations, if possible
Observations of the predicted variable are inferred to have a distribution with this quantile. This statistic is related to the https://en.wikipedia.org/wiki/Prediction_interval getObsQuantile(0.5) is a central statistic for the estimated bias, if the bias is estimated but not corrected.
- quantile
to get, taken between 0.0 and 1.0 (i.e. not a percentile)
- def getUncertainty(observational: Boolean = true): Option[Seq[Any]]
Get the "uncertainty", which is the TotalError if non-observational and the StdDevObs if observational
Get the "uncertainty", which is the TotalError if non-observational and the StdDevObs if observational
- observational
whether the uncertainty should account for observational uncertainty
- returns
uncertainty of each prediction
- Definition Classes
- RegressionResult → PredictionResult
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