What is bucketing

Bucketing in Machine Learning is a technique used to create discrete buckets or bins from continuous data. This is done by dividing the range of values into a series of intervals, and then assigning each data point to a particular bucket. Bucketing is often used to reduce the number of variables in a dataset, as well as to make it easier to group and analyze data.

Bucketing is a form of data preprocessing that can be used to transform continuous values into discrete buckets or bins. This is done by dividing the range of values into a series of intervals, and then assigning each data point to a particular bucket. Bucketing is often used to reduce the number of variables in a dataset, as well as to make it easier to group and analyze data.

The most common way to bucket data is by using a binning technique. This is done by defining a set of bins, or ranges, and then assigning each data point to a bin. For example, if a dataset contains ages, the bins could be defined as 0-18, 19-30, 31-45, and 46 and above. This would allow us to reduce the number of age values to four discrete buckets.

Bucketing can also be used to create new features from existing ones. For example, if a dataset contains the ages of customers, we could bucket the ages into four categories and then create a new feature that only contains the bucket values. This could be used to create a new feature that could be used to group customers together, or to identify any patterns that might exist within the data.

Bucketing is a useful technique for data preprocessing and feature engineering. It can be used to reduce the number of variables in a dataset, as well as to make it easier to group and analyze data. Bucketing can also be used to create new features from existing ones, allowing us to more easily identify patterns and relationships in our data.