# Source

# orange-reliability / docs / rst / Orange.evaluation.reliability.rst

# Reliability estimation (`Orange.evaluation.reliability`)

Reliability assessment statistically predicts reliability of single predictions. Most of implemented algorithms for regression are taken from Comparison of approaches for estimating reliability of individual regression predictions, Zoran Bosnić, 2008. Implementations for classification follow descriptions in Evaluating Reliability of Single Classifications of Neural Networks, Darko Pevec, 2011.

The following example shows basic usage of reliability estimation methods:

- The important points of this example are:
- construction of reliability estimators using classes, implemented in this module,
- construction of a reliability learner that bonds a regular learner (:class:`~Orange.classification.knn.kNNLearner` in this case) with reliability estimators,
- calling the constructed classifier with :obj:`Orange.classification.Classifier.GetBoth` option to obtain class probabilities; :obj:`probability` is the object that gets appended the :obj:`reliability_estimate` attribute, an instance of :class:`Orange.evaluation.reliability.Estimate`, by the reliability learner.

It is also possible to do reliability estimation on whole data table, not only on single instance. Next example demonstrates usage of a cross-validation technique for reliability estimation. Reliability estimations for first 10 instances get printed:

## Reliability Methods

For regression, all the described measures can be used, except for the \(O_{ref}\) . Classification domains are supported by the following methods: BAGV, LCV, CNK and DENS, \(O_{ref}\) .

### Sensitivity Analysis (SAvar and SAbias)

### Variance of bagged models (BAGV)

### Local cross validation reliability estimate (LCV)

### Local modeling of prediction error (CNK)

### Bagging variance c-neighbours (BVCK)

### Mahalanobis distance

### Mahalanobis to center

### Density estimation using Parzen window (DENS)

### Internal cross validation (ICV)

### Stacked generalization (Stacking)

### Reference Estimate for Classification (\(O_{ref}\) )

## Reliability estimation wrappers

## Reliability estimation results

There is a dictionary named :obj:`METHOD_NAME` that maps reliability estimation method IDs (ints) to method names (strings).

In this module, there are also two constants for distinguishing signed and absolute reliability estimation measures:

SIGNED = 0 ABSOLUTE = 1

## Reliability estimation scoring

## Example

This script prints out Pearson's R coefficient between reliability estimates and actual prediction errors, and a corresponding p-value, for each of the reliability estimation measures used by default.

Estimate r p SAvar absolute -0.077 0.454 SAbias signed -0.165 0.105 SAbias absolute 0.095 0.352 LCV absolute 0.069 0.504 BVCK absolute 0.060 0.562 BAGV absolute 0.078 0.448 CNK signed 0.233 0.021 CNK absolute 0.058 0.574 Mahalanobis absolute 0.091 0.375 Mahalanobis to center 0.096 0.349

## References

Bosnić, Z., Kononenko, I. (2007) Estimation of individual prediction
reliability using local sensitivity analysis. *Applied Intelligence* 29(3), pp. 187-203.

Bosnić, Z., Kononenko, I. (2008) Comparison of approaches for estimating
reliability of individual regression predictions. *Data & Knowledge Engineering*
67(3), pp. 504-516.

Bosnić, Z., Kononenko, I. (2010) Automatic selection of reliability estimates
for individual regression predictions. *The Knowledge Engineering Review* 25(1),
pp. 27-47.

Pevec, D., Štrumbelj, E., Kononenko, I. (2011) Evaluating Reliability of
Single Classifications of Neural Networks. *Adaptive and Natural Computing
Algorithms*, 2011, pp. 22-30.