This node draws ROC curves for two-class classification problems. The input table
must contain a column with the real class values (including all class values as possible values)
and a second column with the probabilities that an item (=row) will be classified as being
from the selected class. Therefore only learners/predictors that output class probabilities can
be used.

In order to create a ROC curve for a model, the input table is first sorted by the class probabilities
for the positive class i.e. rows for which the model is certain that it belongs to the positive class
are sorted to front. Then the sorted rows are checked if the real class value is the actually the positive
class. If so, the ROC curve goes up one step, if not it goes one step to the right. Ideally, all positive
rows are sorted to front, so you have a line going up to 100% first and then going straight to right. As a
rule of thumb, the greater the area under the curve, the better is the model.

You may compare the ROC curves of several trained models by first joining the class probability columns
from the different predictors into one table and then selecting several column in the column filter
panel.

The light gray diagonal line in the diagram is the random line which is the worst possible performance a
model can achieve.

- Class column
- Select the column that contains the two classes that the model was trained on.
- Positive class value
- Select the value from the class column that stands for the "positive" class, i.e. the value high probabilities in the probability column (see below) are assigned to.
- Limit data points for each curve to
- By default each curve shows at most 2,000 different data points regardless how may rows are in the input. If you want to see more or less points in the curve, adjust this value. Lower values make rendering the curves faster but this is only an issue if you have many different curves. A value of -1 disables the limit and shows all input data points.
- Columns containing the positive class probabilities
- Select the column(s) that contain the probabilities for the a row being from the positive class.

- ROC Curves
- ROC curves

- 01_Global_Feature_Importance_ExampleKNIME Hub
- 01_Guided_Analytics_for_ML_AutomationKNIME Hub
- 01_Guided_Analytics_for_ML_AutomationKNIME Hub
- 01_Guided_Analytics_for_ML_AutomationKNIME Hub
- 01_Guided_Analytics_for_ML_AutomationKNIME Hub
- Show all 150 workflows

- No links available

You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.

To use this node in KNIME, install the extension KNIME Base nodes from the below update site following our NodePit Product and Node Installation Guide:

v5.3

A zipped version of the software site can be downloaded here.

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud
or on-premises – with our brand new **NodePit Runner**.

Do you have feedback, questions, comments about NodePit, want to support this platform, or want your own nodes or workflows listed here as well? Do you think, the search results could be improved or something is missing? Then please get in touch! Alternatively, you can send us an email to mail@nodepit.com, follow @NodePit on Twitter or botsin.space/@nodepit on Mastodon.

**Please note that this is only about NodePit. We do not provide general support for KNIME — please use the KNIME forums instead.**