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Table Of Contents
Machine Learning For Dummies
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Up to this section, the chapter discusses ensembles made of the same kind of machine learning algorithms, but both averaging and voting systems can also work fine when you use a mix of different machine learning algorithms. This is the averaging approach, and it’s widely used when you can’t reduce the estimate variance.
As you try to learn from data, you have to try different solutions, thus modeling your data using different machine learning solutions. It’s good practice to check whether you can put some of them successfully into ensembles using prediction averages or by counting the predicted classes. The principle is the same as in bagging noncorrelated predictions, when models mixed together can produce less variance-affected predictions. To achieve effective averaging, you have to
- Divide your data into training and test sets.
- Use the training data with different machine learning algorithms.
- Record predictions from each algorithm...
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