A framework for hypothesis spaces and learning algorithms

Framework for hypothesis spaces

Key factors to understand the hypothesis space

  • Size: Is the hypothesis space fixed in size (like Naïve Bayes) or variable in size (like Decision Trees)
    • Fixed size spaces are easier to understand, but variable size spaces are more useful.
    • Variable size spaces introduce the problem of overfitting.
  • Randomness: Is each hypothesis ‘Deterministic’ or ‘Stochastic’? this effects how we evaluate hypothesis.
    • With deterministic hypothesis, the training example is consistent, i.e, correctly predicted OR inconsistent, i.e, incorrectly predicted (ex: spam/non-spam detection)
    • With stochastic hypothesis, the training example is more likely or less likely.
  • Parameterization: Is the hypothesis described by set of symbolic (discrete) choices OR continuous parameters?

discrete_continuous

Framework for Learning Algorithms

Key factors to understand the learning algorithms

  • Search Procedure
    • Direct Computation: Solve for the hypothesis directly
    • Local Search: Start with initial hypothesis and make small improvements until local optimum.
    • Constructive Search: We start with empty hypothesis and gradually add structure to it.
  • Timing
    • Eager: Analyze training data and construct explicit hypothesis (Decision Trees)
    • Lazy: Store the training data and wait till the testing data is presented and then construct the adhoc hypothesis to classify the test instance (KNN Algo).
  • Online vs Batch Learning
    • If the data changes very often (like stock market prediction), we need online learning
    • If the data does not change much overtime (like drug analytics), then we need batch learning. This is more expensive.

 

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