Binary classification output interpretation

binary-classification-output-interpretation

License: restricted — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution. <!-- asb-license-banner -->

Summary

Interpret and compile binary classification predictions (bitter/not-bitter) from a machine-learning classifier into structured output files linked to molecule identifiers. This skill bridges trained model inference and downstream analysis by organizing predictions into a reusable tabular format.

When to use

You have executed a binary classifier (such as BitterPredict.m) on a set of molecules with chemical structure descriptors and need to translate the raw predictions into a structured CSV output file that maps molecule identifiers to their predicted class labels (bitter or not-bitter).

When NOT to use

• The classifier output is already in a structured tabular format with molecule IDs and predictions — no recompilation is needed.
• You need confidence scores or probability estimates rather than binary class labels alone.
• The input molecules lack consistent identifiers that can be reliably mapped to predictions.

Inputs

• Binary classification predictions (bitter/not-bitter) from BitterPredict.m classifier
• Molecule identifiers (from input descriptor file)
• Chemical structure descriptor data (from CSV or Excel input file)

Outputs

• CSV file containing molecule identifiers and predicted class labels (bitter or not-bitter)

How to apply

After BitterPredict.m generates binary bitter/not-bitter predictions for each molecule in your input descriptor set, collect the predicted class labels alongside the corresponding molecule identifiers. Organize these pairs into a structured table with columns for molecule ID and predicted class. Write the compiled table to a CSV output file. Verify that each molecule in the input has exactly one prediction and that all class labels are valid binary values (bitter or not-bitter). The output CSV should maintain the same row order as the input molecules to ensure traceability from input descriptors through prediction to final results.

Related tools

• BitterPredict.m (Executes binary classification on molecular descriptors to generate bitter/not-bitter predictions for each molecule) — https://github.com/Niv-Lab/BitterPredict1

Evaluation signals

• Every molecule identifier from the input descriptor file appears exactly once in the output CSV.
• Every prediction in the output CSV is a valid binary class label (bitter or not-bitter); no null, missing, or invalid values.
• The row order in the output CSV matches the row order of molecules in the input descriptor file, enabling traceability.
• The output CSV is parseable as a valid CSV file with consistent column structure and no formatting errors.
• Spot-check a sample of predictions by re-running BitterPredict.m on a subset of input molecules and confirming predictions match the compiled output.

Limitations

• The skill assumes BitterPredict.m is fully executed before interpretation; if the classifier fails or produces incomplete predictions, the output CSV will be incomplete or incorrect.
• Full code availability is contingent on publication; current repository access may be restricted and users should contact [redacted-email] for availability.
• The CSV output contains only binary class labels; probability scores or confidence metrics from the classifier are not captured.
• Molecule identifiers must be present and consistent across input and output; missing or mismatched IDs will break traceability.

Evidence

• [readme] BitterPredict.m gets as input CSV or EXCEL files with required descriptors of molecules, and calucautes a predictions if each molecule is bitter or not.: "BitterPredict.m gets as input CSV or EXCEL files with required descriptors of molecules, and calucautes a predictions if each molecule is bitter or not."
• [other] Generate binary predictions (bitter or not-bitter) for each molecule. 4. Compile predictions into output CSV file with molecule identifiers and predicted class labels.: "Generate binary predictions (bitter or not-bitter) for each molecule. 4. Compile predictions into output CSV file with molecule identifiers and predicted class labels."
• [readme] BitterPredict is a classifier which predicts whether a compound is bitter or not, based on its chemical structure.: "BitterPredict is a classifier which predicts whether a compound is bitter or not, based on its chemical structure."