Test-Time Augmentation For Tabular Data With Scikit-Learn
Last Updated on August 18, 2020
Test-time augmentation, or TTA for short, is a technique for improving the skill of predictive models.
It is typically used to improve the predictive performance of deep learning models on image datasets where predictions are averaged across multiple augmented versions of each image in the test dataset.
Although popular with image datasets and neural network models, test-time augmentation can be used with any machine learning algorithm on tabular datasets, such as those often seen in regression and classification predictive modeling problems.
In this tutorial, you will discover how to use test-time augmentation for tabular data in scikit-learn.
After completing this tutorial, you will know:
- Test-time augmentation is a technique for improving model performance and is commonly used for deep learning models on image datasets.
- How to implement test-time augmentation for regression and classification tabular datasets in Python with scikit-learn.
- How to tune the number of synthetic examples and amount of statistical noise used in test-time augmentation.
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