Hong Kong Machine Learning Meetup S3E6 - Feature Selection and Importance by Maxence de Rochechouart
When extracting information/making predictions from a large dataset a key step, before choosing and tuning a model, is feature selection. As opposed to including all predictors in the model, a wise variable selection will dramatically reduce training time and improve the performance of the model. In some cases, it can also help making the predictions more interpretable.
After presenting some commonly used ways to select features before choosing a model, Maxence will present feature selection and importance within the framework of random forests, where the model and feature importance are built simultaneously.
Maxence will discuss different methods depending on the nature of the task to be accomplished with the data; for example, to interpret the information from a dataset on a target variable, as many significant features as possible should be included, whereas for prediction tasks, the number of features should be minimised while maximising the performance of the model.
Slides of the talk: https://drive.google.com/file/d/14nSv...
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