Module 6 · Classical ML

Module 6 worked feedback

Compare this with your practice after attempting it. Exact current results are in the generated report; retain the source hash and versions when comparing runs.

A reasoned interpretation

In the initial real-data run, validation selected logistic regression at threshold 0.3. Final counts were TP 19, FP 30, FN 10, TN 184 across 243 records; balanced accuracy was about 0.7575. The shallow tree remains an explanatory comparison, not a post-hoc replacement. Lower thresholds can recover positives while increasing false positives.

What a good explanation contains

State the purpose, observed unit, allowed input, and fitting/selection boundary. Name an actual value or record from the output table, explain its denominator or scale, and state what it cannot establish. Distinguish code correctness, statistical evidence, operational usefulness, and responsible use.

A useful recommendation has this structure: “For [declared task], the recorded workflow produced [actual evidence]. Compared with [relevant alternative], this suggests [limited interpretation]. The unresolved limitation is [specific issue]. Before use, we would [test or decision with an owner].” Fill the brackets from your run; do not invent a favorable score or causal story.

Transfer cautiously

A real IT incident triage task needs genuine incident labels, an investigation cost model, repeated-host boundaries, and timestamps. Wine labels are not incident labels.

If GenAI calls a result “accurate,” “fair,” or “safe,” ask for the actual metric, population, decision rule, and supporting evidence. Reject claims not supported by this study. Return to the lesson or adaptation notes.

Construction check and misconception feedback

Question: If TP=19, FP=30 and FN=10, what are precision and recall?

Precision 19/49 is about .388; recall 19/29 is about .655. They use different denominators.

Judge your answer with the study workflow criteria. If you cannot reproduce the central operation, return to the worked step and try changed inputs before reading the answer again.

Feedback for your own implementation

Use the separate practice solutions and diagnostic feedback to investigate function-level mistakes and compare developing, meeting and extending explanations. This page retains the complete reference study's results.