This slide output results about the binning through some visualizations.

![Numeric Bins.png](xOOVKc6tsiwy)

For numeric bins, one must select one variable at a time in the filter window. The bars show the share of observations in each bin, with values on the right axis while the line represents the weight of evidence of each bin. The x-axis value of each bin is the minimum value of the bin.

![Categorical Bins.png](RwF6vWAWln1Q)

Similarly, for categorical bins, one must also select one variable at a time in the filter window. The bars and line represent the same information as above, but here the names of the bins are the ones defined in the editable dataset.

![Information Value Bins.png](0lofbHiVCk5e)

Finally, the information value bar plot computes as in the previous slide the information value of each variable with respect to the target variable. Information values are smaller than before the binning because this process compresses the information, but it becomes also more reliable. A threshold on the information value is also set to keep only the variables that are most likely to be useful for the modeling.


