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Bucketing: Difference between revisions
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However, it's essential to remember that bucketing may not always be the ideal approach for every situation. Depending on the data and specific analysis objectives, other techniques such as normalization or standardization may be more suitable. | However, it's essential to remember that bucketing may not always be the ideal approach for every situation. Depending on the data and specific analysis objectives, other techniques such as normalization or standardization may be more suitable. | ||
==Example== | |||
For example, instead of representing length as a single continuous floating-point feature, you could chop ranges of lengths into discrete buckets, such as: | |||
<= 30 inches would be the "short" bucket. | |||
31 - 60 inches would be the "medium" bucket. | |||
>= 61 inches would be the "long" bucket. | |||
The model will treat every value in the same bucket identically. For example, the values 37 and 43 are both in the medium bucket, so the model treats the two values identically. | |||
==Types== | ==Types== | ||