Fingerprint Bayesian Learner

Input Ports

The data to learn from. It needs to contain a fingerprint column and a categorical class column.

Output Ports

A table containing the scores of the training data, whereby each row is predicted using a model trained on the n-1 remaining rows (leave-one-out). The table is sorted by descending score; it contains the following columns:

1. The true class values (copied from the input data).
2. The leave-one-out score (the sum-of-logs of the on-bits)).
3. The running error of the target class, i.e. the error on the training data if the current row and all preceding rows were predicted as positive class (as they have a score larger or equal to the row's score).
4. The running error on the negative class(es), i.e. if all rows below the current line were predicted as negative.

The threshold that minimizes the sum of both error rates is used as default cutoff in the predictor.
Note, these scores could also be determined using a Cross-Validation meta node. However, they are provided here as they can be easily computed in a single scan on the training data (as opposed to an expensive cross validation run).
This table can be very well visualized using a ROC Curve node.

A table representing each bit's importance on the different classes. The table has as many rows as there are bits in the fingerprint. The columns show for each bit position, how often a bit is set in (i) any of the rows and (ii) in rows of the respective target class. The value of the "logP" column is the logarithm of equation (6) in the above cited article. A value smaller than 0 indicates that the bit is uncharacteristic for the target class, a value larger 0 shows a strong characteristic for that bit and class. A value ~0 indicates that there is no or a weak relationship between the bit and the class.

The model; it's the input to the predictor node.

Popular Predecessors

• Row Filter (deprecated)6 %
• Concatenate5 %
• RDKit Count-Based Fingerprint4 %
• CSV Reader4 %
• Fingerprints3 %
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Popular Successors

• Model Writer21 %
• Fingerprint Bayesian Predictor10 %
• Scorer9 %
• Model Writer7 %
• Tree Ensemble Learner5 %
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