July 2019
Beginner to intermediate
740 pages
16h 52m
English
Content preview from Hands-On Data Analysis with Pandas
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Pipelines
It sure seems like there are a lot of steps involved in preprocessing our data, and they need to be applied in the correct order for both training and testing data—quite tedious. Thankfully, scikit-learn offers the ability to create pipelines to streamline the preprocessing and ensure that the training and testing sets are treated the same. This prevents issues, such as calculating the mean using all the data in order to standardize it and then splitting it into training and testing sets, which will create a model that looks like it will perform better than it actually will.
When information from outside the training set (such as using the full dataset to calculate the mean for standardization) is used to train the model, it is ...
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