What is Discretize in RapidMiner?
The Discretize By Frequency operator creates bins in such a way that the number of unique values in all bins are (almost) equal. In contrast, the Discretize By Binning operator creates bins in such a way that the range of all bins is (almost) equal.
What is data discretization?
Data discretization is defined as a process of converting continuous data attribute values into a finite set of intervals and associating with each interval some specific data value.
What is the difference between discretization and binning?
Binning can also be used as a discretization technique. Here discretization refers to the process of converting or partitioning continuous attributes, features or variables to discretized or nominal attributes/features/variables/intervals.
Why might we want to discretize an attribute?
Discretizing is transforming numeric attributes to nominal. You might want to do that in order to use a classification method that can’t handle numeric attributes (unlikely), or to produce better results (likely), or to produce a more comprehensible model such as a simpler decision tree (very likely).
What is entropy based discretization?
Entropy-based discretization is a supervised, top-down splitting approach. It explores class distribution data in its computation and preservation of split-points (data values for separation an attribute range).
What is discretization example?
For example, if one-half of the items have a cost of 0, one-half the data will occur under a single point in the curve. In such a distribution, this method breaks the data up in an effort to establish equal discretization into multiple areas. This produces an inaccurate representation of the data.
Should I first discretize dataset by Entropy before cross validation?
As far as I understand discretize by entropy depends on class value and I it would not be correct to first discretize all dataset and then perform cross validation.
Is it possible to use discretize by entropy with Naive Bayes classifier?
I would like to use discretize by entropy operator with naive bayes classifier. As far as I understand discretize by entropy depends on class value and I it would not be correct to first discretize all dataset and then perform cross validation.
How do I perform discretization of induced partitions?
The discretization is performed by selecting bin boundaries such that the entropy is minimized in the induced partitions. This input port expects an ExampleSet. It is output of the Retrieve operator in the attached Example Process.
What is Discretization by Binning?
Then, the discretization by binning is performed only on the values that are within the specified boundaries. The Discretize By Frequency operator creates bins in such a way that the number of unique values in all bins are (almost) equal.