Difference between revisions of "Orange: Calibrated Learner"

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Revision as of 20:31, 12 January 2020

Sumber: https://docs.biolab.si//3/visual-programming/widgets/model/calibratedlearner.html

Wraps another learner with probability calibration and decision threshold optimization.

Inputs

   Data: input dataset
   Preprocessor: preprocessing method(s)
   Base Learner: learner to calibrate

Outputs

   Learner: calibrated learning algorithm
   Model: trained model using the calibrated learner

This learner produces a model that calibrates the distribution of class probabilities and optimizes decision threshold. The widget works only for binary classification tasks.

../../_images/Calibrated-Learner-stamped.png

   The name under which it will appear in other widgets. Default name is composed of the learner, calibration and optimization parameters.
   Probability calibration:
       Sigmoid calibration
       Isotonic calibration
       No calibration
   Decision threshold optimization:
       Optimize classification accuracy
       Optimize F1 score
       No threshold optimization
   Press Apply to commit changes. If Apply Automatically is ticked, changes are committed automatically.

Example

A simple example with Calibrated Learner. We are using the titanic data set as the widget requires binary class values (in this case they are ‘survived’ and ‘not survived’).

We will use Logistic Regression as the base learner which will we calibrate with the default settings, that is with sigmoid optimization of distribution values and by optimizing the CA.

Comparing the results with the uncalibrated Logistic Regression model we see that the calibrated model performs better.

../../_images/Calibrated-Learner-Example.png


Referensi

Pranala Menarik