Data Binning

Publication Date :

07 Jun, 2023

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Table Of Contents

What Is Data Binning?

Data Binning, Bucketing, or Discretization is a data smoothing and pre-processing method to group original continuous data into small, discrete bins, intervals, or categories. Each bin is considered separate so that a general value representing the whole bin can be calculated.

Data binning is commonly employed to manage large datasets or change continuous data to categorical data for analysis or visualization. It limits the overfitting of small datasets, moderates the impact of observation errors to a certain extent, and eliminates noise. To get meaningful results, selecting an appropriate binning technique and bin width is crucial.

What Is Data Binning?

Data binning is a pre-processing method for data smoothing whereby the large set of original data is segregated into intervals called bins, and the discrete values in every bin are worked out to derive a representative value.
It categorizes complex, continuous, and extensive data to decrease noise and reduce the effect of small observation errors on the analysis.
Some widely used bucketing methods are equal-width binning, equal-frequency binning, and custom binning.
Discretization may lead to information loss, over-smoothing, or under-smoothing of datasets, which can further result in misinterpretation and inaccurate outcomes.

Data Binning Explained

Data binning is a way of pre-processing, summarizing, and analyzing data used to group continuous data into discrete bins or categories. It offers several benefits, such as simplifying data analysis and mitigating the impact of outliers in datasets. The process involves dividing the range of values into intervals and assigning each data point to an appropriate bin.

The number and size of bins depend on the discretization technique adopted. It can be determined based on data distribution and specific analysis requirements. However, some techniques call for a fixed number of bins. For instance, the number of bins in percentile binning is 4. Moreover, it is crucial to consider the trade-off between data simplification and the potential loss of details when making a decision about employing binning for analysis.

Steps

Hence, the following general steps can be applied to perform discretization:

Identification of the dataset’s lowest and highest values to establish the range within which the data will be binned.
Choosing a suitable binning method based on the characteristics of the data in question and the objectives of conducting the analysis. Common binning methods include equal-width binning, equal-frequency binning, and custom binning.
Deciding the number of bins based on the level of granularity required, the dataset size needed, or specific analysis requirements defined for the task.
Sorting the data points into appropriate bins based on their values and defined bin boundaries. Depending on the chosen binning method, data points falling on the boundaries can be assigned to a single bin or divided between adjacent bins.
Last but not least is the analysis of data. Once the data points are assigned to bins, the data within each bin can be analyzed separately or used for visualization, such as creating histograms or bar charts.

Techniques

Listed below are some prominent methods of data binning employed by analysts.

Equal-Width Binning: This technique divides the data range into predetermined equal-width intervals or bins. The bin width can be computed by dividing the data range by the selected number of bins. While this method is simple and intuitive, it cannot be applied for skewed data distribution.

Equal-Frequency Binning: In this method, the data is distributed into bins ensuring each bin has roughly the same number of data points. The data is first sorted, and then an equal number of data points is assigned to each bin. This approach is useful when it is essential to maintain similar frequencies or distributions across bins. This binning method can effectively tackle outliers and skewed data.

Entropy-Based Binning: Under this type of discretization, continuous numerical values are categorized such that the clubbed variables represent the same class label. It analyzes the target class label and computes entropy, i.e., data impurities, and categorizes the split based on the level of information gain achievable.

Custom Binning: This method allows users to set bin boundaries based on specific criteria or domain knowledge. Custom binning offers greater flexibility and control over data grouping. For example, bins can be created based on specific value ranges or required categories.

Quantile Binning: A percentile binning technique applies to equal data distribution. It divides the data into bins based on percentiles. Thus, the number of bins is predetermined, and each bin comprises an equal number of data points. The bin boundaries are ascertained by the values at specific percentiles (e.g., 25th, 50th, and 75th percentiles).

Optimal Binning: This bucketing technique aims to identify the most suitable set of bin boundaries based on specific optimization criteria. These methods employ statistical or machine learning algorithms to determine bin boundaries that minimize information loss or maximize desired objectives. For instance, it determines bin boundaries based on a decision tree, chi-square, and Maximum Likelihood Estimation (MLE).

Examples

You may study the following examples to understand the concept.

Example #1

Let us assume that 100 candidates appeared for the Chartered Accountancy exam in a city, and the education department wants to analyze the scores of these candidates age group-wise using data binning. The analyst categorizes the candidates into five bins based on their age. The average score of each bin is stated below:

Bins (Based on Age Group)	Average Scores (0 - 99%)
21 - 30 years	88.4%
31 - 40 years	83.1%
41 - 50 years	77.9%
51 - 60 years	71.7%
61 - 70 years	62.8%

Example #2

The annual return on investment of stock for 12 years is 14%, 13.9%, 13.7%, 14.2%, 14.3%, 14.1%, 13.9%, 14%, 14.4%, 14.5%, 14.7%, and 14.6%, respectively. If the investor wants to analyze the change in ROI every three years, according to the data binning method, the bin size is 3. The data is grouped into the following bins with average ROI:

Bins (Size = 3 years)	Average ROI
14%, 13.9%, 13.7%	13.87%
14.2%, 14.3%, 14.1%	14.20%
13.9%, 14%, 14.4%	14.10%
14.5%, 14.7%, 14.6%	14.60%

Note: The above illustration demonstrates the equal-frequency binning technique.

Advantages And Disadvantages

Data binning is widely used in many fields today. It facilitates data analysis and visualization to simplify information, reduce noise, and enhance manageability. In data mining, it is a key technique applied while dealing with continuous variables.

In Python, it helps address issues related to missing values. Histograms are a popular example of binning in Excel. While this technique offers various benefits, it also has certain drawbacks. Let us discuss these in detail below.

Advantages	Disadvantages
Discretization simplifies complex and large datasets by reducing the number of distinct values.	Binning results in a loss of information, which questions its accuracy since the original values within each bin are replaced with a single representative value, such as the mean or median.
It mitigates the impact of outliers, observation errors, or random fluctuations in data since grouping values into bins results in its stable representation.	Determining the optimal binning configuration can be difficult, and different choices can yield different interpretations since too few bins oversimplify the data. On the other hand, selecting too many bins can lead to overfitting or excessive noise.
It facilitates the conversion of numerical data into categorical variables, thus enabling the use of techniques like decision trees and association rule mining for data analysis.	It is challenging to compare results across different binning strategies due to varying bin sizes.
The compressed data requires less storage capacity.	It is an irreversible process, and if original form data is to be interpreted with different binning criteria, the original granularity cannot be restored.
It reveals patterns and trends to identify relationships, correlations, or clusters within the dataset that may not be readily discernible when working with continuous raw data.	It reduces the statistical power since the resulting data distribution may be distorted, exhibiting artificial patterns, over-smoothing, or under-smoothing, which may not precisely reflect the original data, leading to biased interpretations or misleading conclusions.
It facilitates predictive modeling by converting large data into discrete bins.	The outliers can significantly impact the binning process by stretching the range of a bin or forming separate bins. Hence, the analysis may lose or misrepresent important information contained within outliers.
Binning enhances the interpretability of data analysis outcomes, simplifying summarization. Findings can be easily communicated to the management and other stakeholders.	Binning may not be suitable for all types of data or analysis tasks, necessitating careful consideration of the specific context and objectives of the analysis.

Frequently Asked Questions (FAQs)

1. What are the different types of binning?

There are two types of data binning:
● Supervised binning: Supervised bucketing uses the target class label to convert a numerical or continuous variable into a categorical value through the entropy-based binning technique.
● Unsupervised binning: Unlike supervised binning, this bucketing does not depend on the target class label for categorizing continuous or numerical variables. It includes equal-width binning and equal-frequency binning.

2. What is binning in machine learning?

Binning improves the predictability of machine learning algorithms by sorting the data, addressing non-linearity issues, and reducing noise.

3. Is binning a data-cleaning method?

Binning in data cleaning facilitates data smoothing by categorizing big information into small buckets formed by organizing and consulting the neighborhood, i.e., the surrounding values.

4. Why is bin size important?

The bin or bucket size is crucial in discretization since it decides the accuracy of the outcome of such data analysis. An optimal bin size is between 5 to 20 bins, while the selection depends on the overall size of the original data.