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204.7.2 Ensemble Models

Link to the previous post : https://statinfer.com/204-7-1-random-forests-and-boosting-wisdom-of-crowd/

In this post we will discuss a bit about Ensemble Models and why they work.


Ensemble Models

  • Obtaining a better predictions using multiple models on the same dataset.
  • Not every time it is possible to find single best fit model for our data, ensemble model combines multiple models to come up with one consolidated model.
  • Ensemble models work on the principle that multiple moderately accurate models can give us a highly accurate model.
  • Understandably, the Building and Evaluating the ensemble models is computationally expensive.
  • Build one really good model is the usual statistical approach. Build many models and average the results is the philosophy of Ensemble learning.

Why Ensemble technique works?

  • Imagine three models
    • M1 with an error rate of 10%
    • M2 with an error rate of 10%
    • M3 with an error rate of 10%
  • The three models have to be independent, we can’t build the same model three times and expect the error to reduce. Any changes to the modeling technique in model -1 should not impact model-2.
  • In this scenario, the worst ensemble model will have 10% error rate.
  • The best ensemble model will have an error rate of 2.8%
    • 2 out of 3 models predicted wrong + all models predicted wrong
    • (3C2)*(0.1)(0.1)(0.9) + (0.1)(0.1)(0.1)
    • 2.8% The best ensemble model will have an error rate of 2.8%

In next post we will cover the types of Ensemble Models.

Link to the next post : https://statinfer.com/204-7-3-types-of-ensemble-models/

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