case class BaggedSingleResult(predictions: Seq[PredictionResult[Double]], NibIn: Vector[Vector[Int]], bias: Option[Double] = None, rescale: Double = 1.0) extends BaggedResult[Double] with RegressionResult with Product with Serializable
Container with model-wise predictions and logic to compute variances and training row scores
- predictions
for each constituent model
- NibIn
the sample matrix as (N_models x N_training)
- bias
model to use for estimating bias
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BaggedSingleResult(predictions: Seq[PredictionResult[Double]], NibIn: Vector[Vector[Int]], bias: Option[Double] = None, rescale: Double = 1.0)
- predictions
for each constituent model
- NibIn
the sample matrix as (N_models x N_training)
- bias
model to use for estimating bias
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- val NibIn: Vector[Vector[Int]]
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- val bias: Option[Double]
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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.
- Definition Classes
- RegressionResult
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def
getClass(): Class[_]
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def
getExpected(): Seq[Double]
Return the ensemble average or maximum vote
Return the ensemble average or maximum vote
- returns
expected value of each prediction
- Definition Classes
- BaggedSingleResult → PredictionResult
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def
getGradient(): Option[Seq[Vector[Double]]]
Average the gradients from the models in the ensemble
Average the gradients from the models in the ensemble
- returns
the gradient of each prediction as a vector of doubles
- Definition Classes
- BaggedResult → PredictionResult
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def
getImportanceScores(): Option[Seq[Seq[Double]]]
The importances are computed as an average of bias-corrected jackknife-after-bootstrap and infinitesimal jackknife methods
The importances are computed as an average of bias-corrected jackknife-after-bootstrap and infinitesimal jackknife methods
- returns
training row scores of each prediction
- Definition Classes
- BaggedSingleResult → PredictionResult
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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
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def
getQuantile(quantile: Double, observational: Boolean = true): Option[Seq[Double]]
- Definition Classes
- RegressionResult
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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)
- Definition Classes
- RegressionResult
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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
- Definition Classes
- BaggedSingleResult → RegressionResult
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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.
- Definition Classes
- BaggedSingleResult → RegressionResult
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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)
- Definition Classes
- RegressionResult
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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).
- Definition Classes
- RegressionResult
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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)
- Definition Classes
- RegressionResult
-
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)
- Definition Classes
- RegressionResult
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def
getUncertainty(observational: Boolean): Option[Seq[Any]]
For the sake of parity, we were using this method
For the sake of parity, we were using this method
- observational
whether the uncertainty should account for observational uncertainty
- returns
uncertainty of each prediction
- Definition Classes
- BaggedSingleResult → RegressionResult → PredictionResult
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val
predictions: Seq[PredictionResult[Double]]
- Definition Classes
- BaggedSingleResult → BaggedResult
- val rescale: Double
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