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Bucketing: Difference between revisions
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(Created page with "===Introduction== Bucketing, also referred to as binning, is a data preprocessing technique in machine learning that involves grouping continuous numerical data into discrete categories or "buckets" based on their range of values. This can be beneficial for various reasons such as simplifying the data, eliminating noise and outliers, and improving model accuracy. In this article we'll provide an overview of bucketing in machine learning including its advantages, potentia...") |
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Machine learning involves several different types of bucketing, each with its own advantages and drawbacks. Popular options include: | Machine learning involves several different types of bucketing, each with its own advantages and drawbacks. Popular options include: | ||
===Equal Width Bucketing== | ===Equal Width Bucketing=== | ||
Equal width bucketing is the simplest and most straightforward method of bucketing. It involves dividing a range of values into equal-sized bins, each containing an identical range. For instance, if we have a dataset with values ranging from 0 to 100 and want to create ten bins, each would contain 10 values (i.e., 0-10, 10-20, 20-30, etc). | Equal width bucketing is the simplest and most straightforward method of bucketing. It involves dividing a range of values into equal-sized bins, each containing an identical range. For instance, if we have a dataset with values ranging from 0 to 100 and want to create ten bins, each would contain 10 values (i.e., 0-10, 10-20, 20-30, etc). | ||
One potential drawback of equal width bucketing is that it may result in uneven distributions of data within each bin. For instance, if there are many values within one range, these could be split across multiple bins, potentially decreasing the efficiency of bucketing. | One potential drawback of equal width bucketing is that it may result in uneven distributions of data within each bin. For instance, if there are many values within one range, these could be split across multiple bins, potentially decreasing the efficiency of bucketing. | ||
===Equal Frequency Bucketing== | ===Equal Frequency Bucketing=== | ||
Equal frequency bucketing, also referred to as quantile-based bucketing, is a data storage technique that attempts to divide the data into bins of equal frequency. This means each bin will contain approximately the same number of data points regardless of their range in values; so if we have 100 values and want to create 10 bins, each would contain roughly 10 data points. | Equal frequency bucketing, also referred to as quantile-based bucketing, is a data storage technique that attempts to divide the data into bins of equal frequency. This means each bin will contain approximately the same number of data points regardless of their range in values; so if we have 100 values and want to create 10 bins, each would contain roughly 10 data points. | ||
Equal frequency bucketing offers one advantage over other methods in that it guarantees data is evenly distributed within each bin, improving accuracy in analysis. However, this method may require more computational power if your dataset is very large. | Equal frequency bucketing offers one advantage over other methods in that it guarantees data is evenly distributed within each bin, improving accuracy in analysis. However, this method may require more computational power if your dataset is very large. | ||