![]() One such process is called feature engineering. Results can be improved using constructed sets of application-dependent features, typically built by an expert. Many machine learning practitioners believe that properly optimized feature extraction is the key to effective model construction. Feature extraction is a general term for methods of constructing combinations of the variables to get around these problems while still describing the data with sufficient accuracy. Analysis with a large number of variables generally requires a large amount of memory and computation power, also it may cause a classification algorithm to overfit to training samples and generalize poorly to new samples. ![]() When performing analysis of complex data one of the major problems stems from the number of variables involved.
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