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Bucketing: Difference between revisions
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Bucketing: the process of converting continuous numerical data into discrete forms. To do this, we divide the range of values into equal intervals or bins and assign each data point its appropriate bin based on its value. For instance, if we had a set with 100 values, we might divide it into 10 bins with values ranging from 0-10, 10-20, 20-30 etc. - each data point being assigned its appropriate bin accordingly. | Bucketing: the process of converting continuous numerical data into discrete forms. To do this, we divide the range of values into equal intervals or bins and assign each data point its appropriate bin based on its value. For instance, if we had a set with 100 values, we might divide it into 10 bins with values ranging from 0-10, 10-20, 20-30 etc. - each data point being assigned its appropriate bin accordingly. | ||
Bucketing data simplifies it by reducing its unique values. This can be especially beneficial when working with large datasets or trying to extract patterns from noisy or complex data. Furthermore, bucketing helps mitigate outlier impacts by grouping them within a similar bin, leading to more stable and reliable outcomes. | Bucketing data simplifies it by reducing its unique values. This can be especially beneficial when working with large datasets or trying to extract patterns from noisy or complex data. Furthermore, bucketing helps mitigate outlier impacts by grouping them within a similar bin, leading to more [[stable]] and reliable outcomes. | ||
Bucketing data points could potentially improve the precision of [[machine learning models]]. In certain instances, [[algorithms]] may perform better when data is divided into discrete categories instead of being treated as a continuous variable. This occurs because grouping data points together more efficiently allows the algorithm to identify patterns and connections between them more quickly. | Bucketing data points could potentially improve the precision of [[machine learning models]]. In certain instances, [[algorithms]] may perform better when data is divided into discrete categories instead of being treated as a continuous variable. This occurs because grouping data points together more efficiently allows the algorithm to identify patterns and connections between them more quickly. | ||
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== | ||
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Bucketing or binning is like sorting your toys into distinct boxes. Just as you might have a box for toy cars and another for dolls, bucketing occurs when a computer groups items that are similar. | Bucketing or binning is like sorting your toys into distinct boxes. Just as you might have a box for toy cars and another for dolls, bucketing occurs when a computer groups items that are similar. | ||
[[Category:Terms]] [[Category:Machine learning terms]] | [[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]] | ||