Data Binning

Data Binning (wikipedia)

alternative names: data discrete binning, data bucketing.

Goal of binning is to reduce observation errors. Data falls into a given small interval, called a bin, and then a value replaces that interval, such as mean or median.

In statistics, Statistical Data Binning is a method for grouping continuous values into smaller numbers, bins. Such as age being broken down into smaller groups (bins).

Example Usage

A histogram uses data binning to observe frequency distributions.

Binning is common in MS research, where smalls shifts could be incorrectly interpeted as different components/features. Thus binning ensures the peaks remain in their bin even with slight fluctuations to the bin. (I wonder if these fluctuations hold any data that is lost in binning?)

Also common in machine learning, for supervised classification and regression.

Numerical Data: Binning (google ML)

Binning groups subranges into bins or buckets, and often are used to turn numeric data into categorical data. Example: feature X has lowest value 15 and highest 425 $15\leq X \leq 425$. Thus binning X could look like: 1) 15-34 2) 35-117 3) 118-279 4) 280-392 5) 393-425 In machin learning, a model trained on these bins will react no differently to X values of 17 and 29, as both are Bin 1. If you represent these bins as a feature vectore it would look something like this:

Thus X becomes 5 separate features.

Useful when:

• linear relationship between the feature and lable is weak/none
• feature values are clustered.

Binning in Data Mining (geeksforgeeks)

Why binning is important?

1) data smoothing: reduces impact of minor obvservation variations (noise) 2) outlier mitigation: outliers are reduced as values are grouped into bins 3) improved analysis: making continuous data discrete makes data analysis easier, and improves visualization 4) feature engineering: in ML can help with predictive modeling

Types of Binning Techniques

1. Equal-width binning

Bins are equal width/interval, data is broken into $n$ intervals $\text{Bin Width}= \frac{\text{Max-Value}-\text{Min Value}}{n}$ Easy to implement but does not account for data distribution in bins

2. Equal-frequency binning

Each bin has about same number of data points. Is balanced, but bin width can be highly variable.

Steps in Binning

1. sort data
2. define boundaries for each bin (intervals)
3. Assign data points to bins: put data into bins

Applications of Binning

1. data preprocessing
2. anomaly detection
3. data visualization
4. feature engineering

Challenges of binning

• information loss: since coming from continuous data to discrete, granular detail is lost at points of binning
• subjective: definition of bins is highly subjective
• overfitting: may overfit data, and make a ML model less generalizabile

Data Binning Explained (medium)

Data Binning: organizing data into finite range of intervals. Also called data bucketing.