Calibrated Learner ================== 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) 1. The name under which it will appear in other widgets. Default name is composed of the learner, calibration and optimization parameters. 2. Probability calibration: - [Sigmoid calibration](http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.1639) - [Isotonic calibration](https://scikit-learn.org/stable/auto_examples/plot_isotonic_regression.html) - No calibration 3. Decision threshold optimization: - Optimize classification accuracy - Optimize F1 score - No threshold optimization 4. 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](logisticregression.md) 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)