data_mining:strategy [AE Wiki]

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====== Evaluation metrics and train/dev/test set ======
===== Using a single metric evaluation metric =====

Precision (% of examples recognized as class 1, were class 1)
Recall (% of actual class1 were correctly identified)

  * Classifier A: Precision: 95%, Recall: 90%
  * Classifier B: Precision: 98%, Recall: 85%

Problem: Not sure which classifiers are better (due to tradeoff)
Solution: New Measure which combines both (F1 Score): Harmonic mean $2/((1/p)+(1/r))$, or in general average

Use **Dev set** + **single number evaluation** metric to speed-up iterative improvement

===== Metric tradeoffs =====

Maximize accuracy, subject to runningTime <= 100ms

N metrics: 1 optimizing, N-1 satisficing (reaching some threshold)

===== Train/Dev/Test set =====

Dev set / holdout set: Try ideas on dev set

Goal: Train and esp. dev and test set should come from **same distribution**

Solution: Random shuffle (or stratified sample)

==== Sizes ====
  * For 100 - 10.000 samples: 70 Train 30 Test, or 60% Train 20% Dev 20 % Test
  * For 1.000.000 (NNs): 98% Train, 1% Dev, 1% Test

===== Change dev/test set and metric =====

Change metric, if rank ordering isn't "right"

One solution: Use weights for certain errors

Two steps:

  - Place the target (eval metric)
  - How to shoot at target (how to optimize metric)

E.g. high quality images in dev/test set, user upload low quality images. => change metric and/or dev/test set

====== Human level performance ======

Bayes optimal error (best optimal error)

Human level error could be used as an estimate for Bayes error (e.g. in Computer Vision)

  * H: 1%, Train: 8%, Dev: 10% => bias reduction
  * H: 7,5%, Train: 8, Dev: 10% => variance reduction (more data, regularization)

What's human-level error? Best performance possible as a human / usefullness

Measure of error between Human Error, Train Error and Dev error

  * Avoidable bias: Human level <> Training Error
    * Train bigger model
    * Train longer/better opti algos
    * NN architecture/hyperparam search
  * Variance: Training Error <> Dev Error
    * More data
    * Regularization
    * NN architecture/hyperparam search