Machine Learning Methods.

What a machine learning model is doing when it fits, and the ways it fails silently. Covers 10 sub-skills.

• Machine Learning Methods · 1 of 3

  Model Evaluation Metrics

  What is a 'false negative' in simple terms?

  AWhen the model says 'no' but the correct answer is 'yes'BWhen the model says 'yes' but the correct answer is 'no'CWhen the model cannot make a prediction due to missing featuresDWhen the model correctly predicts a negative outcome

• Machine Learning Methods · 2 of 3

  Overfitting & Regularization

  What is the correct decomposition of total prediction error?

  ATotal Error = Bias² + Variance + Irreducible ErrorBTotal Error = Bias² × Variance, taken as a productCTotal Error = (Bias + Variance)² + Irreducible ErrorDTotal Error = Bias + Variance, with no third term

• Machine Learning Methods · 3 of 3

  Training & Validation

  You bootstrap 95% confidence intervals on a single test set's metric. Comparing this with bootstrapping inside cross-validation folds, what does the single-set bootstrap miss?

  AVariance from which examples landed in the held-out partition itselfBVariance from which seeds landed in the chosen sampler weights itselfCVariance from which features landed in the input column subset itselfDVariance from which optimiser landed in the chosen schedule grid itself