Machine learning predictive modeling performance is only as good as your data, and your data is only as good as the way you prepare it for modeling.<\/p>\n
The most common approach to data preparation is to study a dataset and review the expectations of a machine learning algorithm, then carefully choose the most appropriate data preparation techniques to transform the raw data to best meet the expectations of the algorithm. This is slow, expensive, and requires a vast amount of expertise.<\/p>\n
An alternative approach to data preparation is to apply a suite of common and commonly useful data preparation techniques to the raw data in parallel and combine the results of all of the transforms together into a single large dataset from which a model can be fit and evaluated.<\/p>\n
This is an alternative philosophy for data preparation that treats data transforms as an approach to extract salient features from raw data to expose the structure of the problem to the learning algorithms. It requires learning algorithms that are scalable of weight input features and using those input features that are most relevant to the target that is being predicted.<\/p>\n
This approach requires less expertise, is computationally effective compared to a full grid search of data preparation methods, and can aid in the discovery of unintuitive data preparation solutions that achieve good or best performance for a given predictive modeling problem.<\/p>\n
In this tutorial, you will discover how to use feature extraction for data preparation with tabular data.<\/p>\n
After completing this tutorial, you will know:<\/p>\n
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- Feature extraction provides an alternate approach to data preparation for tabular data, where all data transforms are applied in parallel to raw input data and combined together to create one large dataset.<\/li>\n
- How to use the feature extraction method for data preparation to improve model performance over a baseline for a standard classification dataset.<\/li>\n
- How to add feature selection to the feature extraction modeling pipeline to give a further lift in modeling performance on a standard dataset.<\/li>\n<\/ul>\n
Discover data cleaning, feature selection, data transforms, dimensionality reduction and much more in my new book<\/a>, with 30 step-by-step tutorials and full Python source code.<\/p>\n
Let’s get started.<\/p>\n
![\"How](\"https:\/\/machinelearningmastery.com\/wp-content\/uploads\/2020\/07\/How-to-Use-Feature-Extraction-on-Tabular-Data-for-Data-Preparation.jpg\")
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