binning

• Binning (also called bucketing) is a [[Feature Engineering]] technique that groups different numerical subranges into bins or buckets. I
• n many cases, binning turns numerical data into categorical data.

For example, consider a feature named X whose lowest value is 15 and highest value is 425. Using binning, you could represent X with the following five bins:

• Bin 1: 15 to 34
• Bin 2: 35 to 117
• Bin 3: 118 to 279
• Bin 4: 280 to 392
• Bin 5: 393 to 425

Binning is a good alternative to [[Normalization|scaling]] or [[Clipping]] when either of the following conditions is met:

• The overall linear relationship between the feature and the label is weak or nonexistent.
• When the feature values are clustered.

Binning can feel counterintuitive, given that the model in the previous example treats the values 37 and 115 identically. But when a feature appears more clumpy than linear, binning is a much better way to represent the data.

Quantile bucketing creates bucketing boundaries such that the number of examples in each bucket is exactly or nearly equal.

• Quantile bucketing mostly hides the outliers.
• Bucketing with equal intervals works for many data distributions.
• For skewed data, however, try quantile bucketing.
• Equal intervals give extra information space to the long tail while compacting the large torso into a single bucket.
• Quantile buckets give extra information space to the large torso while compacting the long tail into a single bucket.