Shapley Values Loop End

Aggregates the predictions per row to be explained and calculates the Shapley Values for each feature prediction combination. For each explained row (top inputs of the Shapley Values Loop Start node), this node outputs number of prediction columns rows where each row contains the explanation for one prediction column. This kind of explanation row consists of the four columns "RowId", "Target", "Actual prediction", "Deviation from mean prediction", and a row for each feature (selected in the Shapley Values Loop Start node). The meanings of these are as follows:

• RowId: The RowId of the row of interest.
• Target: The name of the prediction column that is explained.
• Actual prediction: The actual prediction your model created for the row of interest.
• Deviation from mean prediction: The difference between the prediction of the row of interest and the mean prediction of the model. The mean prediction is estimated from the model's predictions on the sampling table (bottom input of the Shapley Values Loop Start node).

Options

Automatically detect prediction columns

With this option the node will use all numeric columns that were added in the loop body as prediction columns.

Manually select prediction column

Allows you to manually select the prediction columns among the input columns of this node.

Order column

Allows to select the order column if its name has been altered in the loop body. By default the order column produced by the loop start is selected.

Use element names for collection features

Collection and vector columns can store names for the individual elements they contain. By checking this box, these names will be used in the explanations produced by this node. Otherwise the columns corresponding to collection features will be named according to their collection followed by an index. Note that we also use the latter naming strategy if the number of names stored in a collection column is different from the number of elements in the collection.

Input Ports

Table containing predictions for the perturbed rows produced by the Shapley Values Loop Start node

Output Ports

Table containing the Shapley Values for each feature-prediction combination

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Workflows

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Installation

To use this node in KNIME, install KNIME Machine Learning Interpretability Extension from the following update site:

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

You don't know what to do with this link? Read our NodePit Product and Node Installation Guide that explains you in detail how to install nodes to your KNIME Analytics Platform.

Developers

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