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| ==Introduction== | | <gallery mode="packed"> |
| A feature cross is a machine learning technique that adds interaction features between features in a dataset. This technique can be used to capture non-linear relationships among features and improve performance of a model.
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| ==How it works==
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| A feature cross creates new features by combining two or more existing features. If we have two features, for example, x1 + x2, a feature crossover would create a new feature, which is x1 *x2. This new feature captures the interaction of x1-x2 and can improve a model's performance.
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| ==Examples==
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| A feature cross can be seen in the context polynomial regression. Polynomial regression allows us to add polynomial terms such as x12,x1*x2,x22, and so on. to capture non-linear relationships among the features.
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| Another example is the context of natural-language processing (NLP), which allows us to use feature crosses to capture interaction between different words within a sentence. In the sentence "The cat sat on a mat", we can create new features to capture the interaction between "cat", "sat", or "on" and the "mat."
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| ==Explain Like I'm 5 (ELI5)==
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| A feature cross is a way to combine words. If you have the words "cat" or "sat", you could make a new word by adding them together. This can help a computer better understand the relationship between words, especially if they are related.
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