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What is feature correlation?
Positive Correlation: means that if feature A increases then feature B also increases or if feature A decreases then feature B also decreases. Negative Correlation: means that if feature A increases then feature B decreases and vice versa. No Correlation: No relationship between those two attributes.
What is CFS in machine learning?
CFS (Correlation based Feature Selection) is an algorithm that couples this evaluation formula with an appropriate correlation measure and a heuristic search strategy. Three machine learning algorithms were used: C4. 5 (a decision tree learner), IB1 (an instance based learner), and naive Bayes.
How does a correlation based feature selection algorithm work?
The algorithm uses the idea of “predominant correlation”. It selects features with high correlation with the target and little correlation with other variables. It does not use the classical Pearson or Spearman correlations, but a metric called Symmetrical Uncertainty (SU).
Which is an example of a correlation based filter?
The FCBF package is an R implementation of an algorithm developed by Yu and Liu, 2003 : Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution. The algorithm uses the idea of “predominant correlation”. It selects features with high correlation with the target and little correlation with other variables.
How are feature importances used in feature selection?
We can also use feature importances for feature selection by removing zero importance features. In a tree-based model, the features with zero importance are not used to split any nodes, and so we can remove them without affecting model performance.
Which is the Best Feature selection tool for machine learning?
The next method is designed only for supervised machine learning problems where we have labels for training a model and is non-deterministic. The identify_zero_importance function finds features that have zero importance according to a gradient boosting machine (GBM) learning model.