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Async review

Instruction and application
In Progress

Recap core topics:​

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Unit 1:

Performance Metric Selection and Implementation

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Unit 2:

Performance Optimisation and Model Refinement

Unit 1: Performance Metric Selection and Implementation​

In Unit 1, you explored…

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Performance metrics selection

  • Single-View Snapshot – How F1-score balances classification; and how R² reveals regression explainability.

  • Business alignment in metric selectionFalse-positive phobia – When too many false alarms erode trust, dial up precision.

  • False-negative fear – If missing a hit is disaster, crank recall to catch every signal.

  • Balanced battleground – When both misfires matter, wield the F1-score as your unified gauge.

Unit 2: Performance Optimisation and Model Refinement

In Unit 2, you explored…

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Cross-validation techniques

  • Preserve class proportions in each fold with Stratified Cross-Validation to avoid bias in imbalanced classification.

  • Aggregating results across folds delivers more robust, unbiased performance estimates than a single train/test split.

  • Use standard K-Fold for fairly uniform data; opt for stratification when class imbalance might skew your evaluation.

  • Error analysis methodsConfusion matrices decompose predictions into TP, FP, FN, TN to pinpoint exactly where your model errs.

  • ROC curves plot true-positive rate against false-positive rate across all thresholds, with AUC summarising overall discrimination.

  • Use confusion matrix insights to choose and tune metrics (Precision, Recall) based on specific error costs.

  • Leverage ROC/AUC threshold analysis to select the optimal operating point for your business priorities.

Knowledge check​

Let's see how much you can remember about async units 1 and 2.

Try taking the quiz below, remember it's not a test, it's to help you find out if there are any areas you should brush up on.

In a fraud-detection system where false alarms are costly, which metric measures the proportion of predicted positives that are actually true?

When you need to heavily penalise large deviations in your regression predictions, which metric should you choose?

To chart unbiased model performance across imbalanced classes, which cross-validation warp drive keeps class proportions consistent in each fold?

When comparing two classifiers on the same test set and you need to know if their misclassification patterns truly diverge beyond chance, which statistical test do you launch?

On the bias-variance nebula, which technique most directly reduces variance by averaging multiple models' predictions?

In progress