(the lady in the picture is a metaphor for the "feature" or "set of features" in the dataset at hand)
If data science, a feature is a variable that has been cleaned and processed so that it's ready to use in a model. Most data models are sensitive to their inputs' scale, so most features are normalized before they are useful in these models. Naturally, not all features add value to a model or a data science project in general. That's why we often need to evaluate them, in various ways, before we can proceed with them in our project.
Evaluating features is an often essential part of the data engineering phase in the data science pipeline. It involves comparing them with the target variable in a meaningful way to assess how well they can predict it. This assessment can be done by either evaluating the features individually or in combination. Of these approaches, the first one is more scalable and more manageable to perform. Since there are inevitable correlations among the features, the individual approach may not paint the right picture, since two "good" features may not work well together since they depict the same information. That's why evaluating a group of features is often better, even if it's not always practical.
Note that the usefulness of a feature usually depends on the problem at hand. So, we need to be clear as to what we are trying to predict. Also, even though a good feature is bound to be useful in all the data models involved, it's not utilized the same way. So, having some intimate understanding of the models can be immensely useful for figuring out what features to use. What's more, the form of the feature is also essential in its value. If a continuous feature is used as is, its information is utilized differently than if it is binarized, for example. Sometimes, the latter is a good idea as we don't always care for all the information the feature may contain. However, it's best not to binarize continuous features haphazardly since that may limit the data models' performance.
The methodology involved also plays a vital role in the feature evaluation. For example, if you perform classification, you need to assess your features differently than if you are performing regression. Also, note that features play a different role altogether when performing clustering as the target variable doesn't participate (or it's missing altogether). As a result of all this, evaluating features is crucial for dimensionality reduction, a methodology closely linked to it and usually follows it.
You can learn more about features and their use in predictive models in my latest book, Julia for Machine Learning. This book explores the value of data from a machine learning perspective, with hands-on application of this know-how on various data science projects. Feature evaluation is one aspect of all this, which I describe through the use of specialized heuristics. Check out this book when you have a chance and learn more about this essential subject!
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Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.