.. _`ROC from Probabilities`:

.. _`org.sysess.sympathy.machinelearning.roc_prob`:

ROC from Probabilities
``````````````````````

.. image:: roc_curve.svg
   :width: 48


Computes Receiver operating characteristics (ROC) based on calculated Y-probabilities and from true Y.




Documentation
:::::::::::::

The ROC (receiver operating characteristic) curve is a graphical plot which illustrates the
performance of a binary classifier system as its discrimination threshold is varied. It is
created by plotting the fraction of true positives out of the positives (TPR = true positive
rate) vs. the fraction of false positives out of the negatives (FPR = false positive rate),
at various threshold settings.

The threshold will typically vary from 1 (or infinity), where only classifications with 100%
confidence will be considered as correct, to 0 where all classifications will be considered
correct. Thus the curve will always go from (TPR=0.0, FPR=0.0) to (TPR=1.0, FPR=1.0). A good
classifier will have a high TPR for all thresholds, while a bad (random) classifier will yield
a linear roc curve from (0, 0) to (1, 1).


Definition
::::::::::


Input ports
...........

    **Y-prob**
        | Type: table
        | Description: Y-prob
    **Y-true**
        | Type: table
        | Description: Y-true

Output ports
............

    **roc**
        | Type: table
        | Description: roc



Configuration
.............

    **Drop suboptimal thresholds** (drop_intermediate)
        Whether to drop some suboptimal thresholds which would not appear
        on a plotted ROC curve. This is useful in order to create lighter
        ROC curves.

        .. versionadded:: 0.17
           parameter *drop_intermediate*.
    **header as label** (header as label)
        Use header of Y-prob as the target label
    **Positive class label** (pos_label)
        The label of the positive class.
        When ``pos_label=None``, if `y_true` is in {-1, 1} or {0, 1},
        ``pos_label`` is set to 1, otherwise an error will be raised.







Examples
........





The node can be found in:

* :download:`Metrics.syx </Examples/Sympathy for Data/MachineLearning/Metrics.syx>`




Implementation
..............

.. automodule:: node_metrics
    :noindex:

.. class:: ROCFromProb
    :noindex: