When working on a security‑oriented classification task that combines free‑form skill descriptions with scanner‑derived numeric signals, the main hurdles are preparing heterogeneous data, avoiding leakage, and quickly spotting where the model goes wrong. Start by making a clean copy of your dataframes, filling missing skill text with an empty string and truncating to a sensible length (e.g., 6000 characters) to keep memory usage low. Convert every numeric column to a proper float type, coercing errors to NaN so they can be imputed later.
Next, build a two‑branch pipeline: one for the text using TF‑IDF with unigrams and bigrams, limiting features to 20 000, dropping very rare terms, and applying sub‑linear TF scaling; the other for the numbers imputing missing values with zero and standard scaling. Combine both branches with ColumnTransformer, then feed the output into a Logistic Regression classifier set to handle class imbalance, allow many iterations, and use a multinomial loss for multi‑class verdicts.
Fit the model on the training split, predict on the hold‑out set, and instantly review a classification report to see precision, recall, and F1 per class. Plot a confusion matrix heatmap to visualise which verdicts are most often confused. Finally, extract a handful of mis‑classified examples, display the skill slug, true label, and prediction, and inspect the raw text or numeric features to understand why the model erred—often due to rare phrasing or out‑of‑range scanner counts. Iterate by adjusting TF‑IDF parameters, trying different imputation strategies, or experimenting with a stronger model if needed.
This end‑to‑end workflow gives you a reproducible baseline, clear diagnostics, and a fast path to improve performance on your skill‑based security classifier.
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