The RFECV algorithm recursively takes the best and worst of these things in order to produce a ranking of them at the end of execution.  
The RFECV algorithm recursively takes the best and worst of these things in order to produce a ranking of them at the end of execution.  An algorithm named for transforming these things was developed by David Lowe and is invariant to rotation and scale.  The relative usefulness of these things is printed in the output of the CfsSubsetEval function in (*) weka.  The sklearn ["S-K-learn"] toolkit uses a randomized LASSO algorithm in order to implement the stability selection technique for reducing the number of them.  Weak examples of these things get assigned coefficients near zero in L1 regularization.  These things might be generated through a blob or edge detection algorithm while "extracting" them in an image processing task.  These things might include n-grams of words in a document classification problem.  For 10 points, name these individual properties which are plugged into machine learning algorithms.
The RFECV algorithm recursively takes the best and worst of these things in order to produce a ranking of them at the end of execution.  
model features  [or attributes or dimensions or parameters] 