Notes on "Lessons from 2MM machine learning models"

Not a comprehensive outline of the talk, just a list of points that I found interesting.

  • During the timeline of a competition, scores reach a plateau or floor where subsequent increases in accuracy are minimal
    • The "four-minute mile" phenomenon: when someone makes a breakthrough that dramatically pushes past a plateau, it is immediately replicated by others
    • Otherwise the floor represents the limits of the signal in the dataset
    • Usually the floor is reached unless there is too much noise or not enough signal in the dataset
  • Neural networks are dominating in any competition involving images, speech, or text
  • Two approaches to winning
    • Creative feature engineering: make plots, test many different combinations of features, use version control to keep track
      • E.g. used car competition, where winning model depended on the crucial feature of unusual car colors vs. standard car colors
    • Parameter tuning: usually only gets incremental improvements in score
  • XGBoost (variant on gradient boosting) also dominating in competitions
  • To guard against overfitting, final scoring of submissions uses completely new test data
    • Overfitting is the most common issue in supervised learning problems
    • Phenomenon where someone high up on leaderboard drops a hundred places after final scoring
    • Can guard against overfitting by ignoring feedback from parameter tuning unless score improves above standard error
  • How are test sets generated?
    • Out-of-time sampling
    • Out-of-sample sampling
    • Stratified sampling (if one of the classes being predicted is very rare in the dataset)
  • Boundaries between different types of problems: which ones suited for neural network approachvs XGBoost/random forests/etc. approach?
    • Unstructured data for former, very structured data for latter
    • What about in-between cases? e.g. EEG data for grasping vs lifting: time series data where neural networks won
  • Any way to automate feature engineering? - a hard problem...
  • Optimizing behavior in response to machine learning results? - also a hard problem...
  • Kaggle Scripts as a learning resource, "Github for data science"
  • Properties of Kaggle winners: good coders, careful use of version control, coding best practices, tenacity

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