What is Feature Impact in Machine Learning?
Depending on their properties, different machine learning algorithms focus on different features in a dataset. For instance, features that have strong linear trends (that is, they increase or decrease at a steady rate) will have high impacts in linear-based methods like regression, while nonlinear-based methods will leverage the more complex relationships in the data. Data science practitioners apply various techniques for investigating which features are meaningful to improving the accuracy and applicability of their models.
Why is Feature Impact important?
In the age of big data, the size and dimensionality of modern datasets are astronomical. Assessing which pieces of information are most crucial allows analysts and business professionals to focus on the factors that matter the most, saving time and resources. Not only that, identifying the essential drivers of a machine learning model’s outcomes helps you check the quality of your data source. For example, if your organization is paying for data to use in machine learning and artificial intelligence (AI) initiatives from an expensive third party but feature impact analysis indicates that none of the data are useful, you save thousands of dollars.
Traditionally, only certain machine learning algorithms could be used for feature impact while others are deemed too much of a “black-box,” giving no insight into why or how they arrived at their outcomes. This makes it difficult to justify why you didn’t include some features in your dataset, which is especially problematic in highly regulated industries like insurance and healthcare.
Additionally, feature impact is used in both feature selection, one of the best ways to improve the accuracy of your models, and identifying target leakage, one of the best ways to avoid highly inaccurate models. If a single feature is extremely impactful on a model’s outcomes, that is a primary indicator of target leakage and warrants further analysis.
Feature Impact + DataRobot
The DataRobot automated machine learning platform sheds light on which features are most important to any machine learning algorithm the platform builds, eliminating the black box problem. It uses permutation importance to estimate feature impact with the click of a button, which means it is model agnostic and can be calculated for any approach, no matter how complicated. This allows users to leverage the most sophisticated machine learning algorithms while also making sure the resulting models are highly human-interpretable without sacrificing value.