Chandan Singh | Evaluation

evaluation

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losses

  • define a loss function $\mathcal{L}$
    • 0-1 loss: $\vert C-f(X)\vert$ - hard to minimize (combinatorial)
    • $L_2$ loss: $[C-f(X)[^2$
  • risk = $E_{(x,y)\sim D}[\mathcal L(f(X), y) ]$
  • optimal classifiers
    • Bayes classifier minimizes 0-1 loss: $\hat{f}(X)=C_i$ if $P(C_i\vert X)=max_f P(f\vert X)$
    • KNN minimizes $L_2$ loss: $\hat{f}(X)=E(Y\vert X)$
  • classification cost functions
    1. misclassification error - not differentiable
    2. Gini index: $\sum_{i != j} p_i q_j$
    3. cross-entropy: $-\sum_x p(x): \log : \hat p(x) $, where $p(x)$ are usually labels and $\hat p(x)$ are softmax outputs
      1. only penalizes target class (others penalized implicitly because of softmax)
      2. for binary, $- (p \log \hat p + (1-p) \log (1-\hat p)$

measures

goodness of fit - how well does the learned distribution represent the real distribution?

Screen Shot 2019-06-30 at 8.27.56 PM

  • accuracy-based
    • accuracy = (TP + TN) / (P + N)
      • correct classifications / total number of test cases
    • balanced accuracy = 1/2 (TP / P + TN / N)
  • denominator is total pos/neg
    • recall = sensitivity = true-positive rate = TP / P = TP / (TP + FN)
      • what fraction of the real positives do we return?
    • specificity = true negative rate = TN / N = TN / (TN + FP)
      • what fraction of the real negatives do we return?
    • false positive rate = FP / N $= 1 - \text{specificity}$
      • what fraction of the predicted negatives are wrong?
  • fraction is total predictions
    • precision = positive predictive value = TP / (TP + FP)
      • what fraction of the prediction positives are true positives?
    • negative predictive value = TN / (FN + TN)
      • what fraction of predicted negatives are true negatives?
  • F-score is harmonic mean of precision and recall: 2 * (prec * rec) / (prec + rec)
  • curves - easiest is often to just plot TP vs TN or FP vs FN

    • roc curve: true-positive rate (recall) vs. false-positive rate
      • perfect is recall = 1, false positive rate = 0
    • precision-recall curve
    • AUC: area under (either one) of these curves - usually roc

comparing two things

  • odds: p1 : not p1
  • odds ratio is a ratio of odds

cv

  • cross validation - don’t have enough data for a test set
    • properties
      • not good when n < complexity of predictor
      • because summands are correlated
      • assume data units are exchangeable
      • can sometimes use this to pick k for k-means
      • data is reused
    • types
      1. k-fold - split data into N pieces
        • N-1 pieces for fit model, 1 for test
        • cycle through all N cases
        • average the values we get for testing
      2. leave one out (LOOCV)
        • train on all the data and only test on one
        • then cycle through everything
      3. random split - shuffle and repeat
      4. one-way CV = prequential analysis - keep testing on next data point, updating model
      5. ESCV - penalize variance between folds
  • regularization path of a regression - plot each coeff v. $\lambda$

    • tells you which features get pushed to 0 and when
  • for OLS (and maybe other linear models), can compute leave-one-out CV without training separate models

stability

  1. computational stability
    • randomness in the algorithm
    • perturbations to models
  2. generalization stability
    • perturbations to data
    • sampling methods
    1. bootstrap - take a sample
      • repeatedly sample from observed sample w/ replacement
      • bootstrap samples has same size as observed sample
    2. subsampling
      • sample without replacement
    3. jackknife resampling
      • subsample containing all but one of the points

other considerations

  • computational cost
  • interpretability
  • model-selection criteria
    • adjusted $R^2_p$ - penalty
    • Mallow’s $C_p$
    • $AIC_p$
    • $BIC_p$
    • PRESS