Contents
What are interactions in machine learning?
1 Feature Interaction? If a machine learning model makes a prediction based on two features, we can decompose the prediction into four terms: a constant term, a term for the first feature, a term for the second feature and a term for the interaction between the two features.
What is local feature importance?
For regression models, the local feature importance for a prediction tells you how much each feature added to or subtracted from the result as compared with the baseline prediction score. Local feature importance is available for both online and batch predictions.
What is interaction feature?
Feature interaction is a software engineering concept. It occurs when the integration of two features would modify the behavior of one or both features. The term feature is used to denote a unit of functionality of a software application.
Why feature selection is used?
Feature selection is the process of reducing the number of input variables when developing a predictive model. It is desirable to reduce the number of input variables to both reduce the computational cost of modeling and, in some cases, to improve the performance of the model.
Why is the interaction between two features important?
This is also a disadvantage because the importance of the interaction between two features is included in the importance measurements of both features. This means that the feature importances do not add up to the total drop in performance, but the sum is larger.
How is the importance of a feature determined?
Feature Importance. Feature importance refers to a class of techniques for assigning scores to input features to a predictive model that indicates the relative importance of each feature when making a prediction.
How is the relative score of a feature useful?
The relative scores can highlight which features may be most relevant to the target, and the converse, which features are the least relevant. This may be interpreted by a domain expert and could be used as the basis for gathering more or different data. Feature importance scores can provide insight into the model.
Which is an example of feature interaction in machine learning?
For example, a model predicts the value of a house, using house size (big or small) and location (good or bad) as features, which yields four possible predictions: