Please tell us about feature selection and enlighten us.
One of the built-in feature selection algorithms, out-of-bag for random forests in classification trees, was employed to select features for machine learning (Selecting predictors for random forests – MATLAB & Simulink – MathWorks Japan) to indicate the importance of the predictors.
A histogram was created and sorted in order of the predictors’ values, and from the histogram, the predictors were fed into the machine learning model by identifying where the importance of the predictors differed significantly and selecting only those that were greater than or equal to the histogram.
Here, can we consider the method of finding the histograms, the parts that differ significantly visually, and selecting thresholds as one of the various options for narrowing down the number of features?
We would be very grateful if you could provide us with some guidance.
Thank you very much in advance.Please tell us about feature selection and enlighten us.
One of the built-in feature selection algorithms, out-of-bag for random forests in classification trees, was employed to select features for machine learning (Selecting predictors for random forests – MATLAB & Simulink – MathWorks Japan) to indicate the importance of the predictors.
A histogram was created and sorted in order of the predictors’ values, and from the histogram, the predictors were fed into the machine learning model by identifying where the importance of the predictors differed significantly and selecting only those that were greater than or equal to the histogram.
Here, can we consider the method of finding the histograms, the parts that differ significantly visually, and selecting thresholds as one of the various options for narrowing down the number of features?
We would be very grateful if you could provide us with some guidance.
Thank you very much in advance. Please tell us about feature selection and enlighten us.
One of the built-in feature selection algorithms, out-of-bag for random forests in classification trees, was employed to select features for machine learning (Selecting predictors for random forests – MATLAB & Simulink – MathWorks Japan) to indicate the importance of the predictors.
A histogram was created and sorted in order of the predictors’ values, and from the histogram, the predictors were fed into the machine learning model by identifying where the importance of the predictors differed significantly and selecting only those that were greater than or equal to the histogram.
Here, can we consider the method of finding the histograms, the parts that differ significantly visually, and selecting thresholds as one of the various options for narrowing down the number of features?
We would be very grateful if you could provide us with some guidance.
Thank you very much in advance. t feature selection MATLAB Answers — New Questions

​