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# Questions tagged [binning]

Binning means grouping a continuous variable into discrete categories. It is particularly used in reference to histograms

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14 views

### WoE optimal binning algorithm [closed]

I'm trying to translate this thing into a working algorithm. The paper here describes the algorithm implemented in MatLab. There is an alleged python translation here. but I never managed, or saw ...
16 views

### How to treat low frequency continuous variable in machine leanring

Hello I am working on machine learning model for count data, and I have various features that are highly skewed. The frequency table for one of the feature is given below. ...
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### Optimal multivariate binning where the cut-points must be the same for all observations

I have a large data set with many discrete and continuous variables. All the variables are present in every observation. I want to explain (the log of) one continuous variable using all the other ...
139 views

### A data-independant transformation to discretize a range of values non-uniformly

I am sure this is trivial, but I am looking for a transformation that nonuniformly discretizes all values of a range into several bins. The bins should be variant and I'd like them to be smaller ...
17 views

### How to calculate errors for cumulative distribution function

I have some data points of the form $(x_i,y_i,\delta y_i)$, where $y$ are counts and the error associated to each $y_i = N$ is $y_i = \sqrt{N}$. I want to create the cumulative distribution of these ...
24 views

### Logistic regression interpretation in SPSS statistics

I observed a very strange behavior while doing logistic regression, univariance analysis and correlation analysis. I have dependent binary variable and several independent variables that should be ...
41 views

1k views

### Creating a Predictive Model with Binned Data

I have a health dataset with the number of drinks per month someone consumes, and many other variables that are binned. For example, 1: income less than \$10000, 2=income less than \$20000, and so on. ...
524 views

### Logarithmic binning and log-normal distribution

I've an Italian cities dataset. It's similar to those British ones used in literature, but has some differences, though. I decided to perform a logarithmic binning to avoid noise on the right end of ...
180 views

### How to fit a distribution to binned values that come from administrative data?

Fitting a distribution to data (e.g. with maximum likelihood), or testing goodness of fit (e.g. with Kolmogorov-Smirnov) assumes that the data are randomly drawn from a population. But what if the ...
33 views

### How creating bins for a numeric feature can enables the model to learn nonlinear relationships within a single feature?

I understood How binning of numerical feature would help build correlations between the feature & the predictor. For example For a regression problem, we can bucketize "population" feature into ...
1k views

### Converting a continuous variable to categorical

I have several continuous predictor variables and one binary outcome variable. One of these predictor variables has the following description using the Hmisc package: ...
97 views

### Splitting range of independent variable to maximize prediction within the subranges

I have a dataset with two independent variables $X,Z \in \mathbb{R}$ and a dependent variable $Y \in \mathbb{R}$. This dataset has the following characteristics: given some number $z$ and a "small" \$...
790 views

### Is there a general/golden rule for appropriate binning in a histogram?

I was wondering, is there a general rule or a "golden rule" that sets the appropriate bin size as a function of statistical parameters such as sample size, mean, median, mode, standard deviation, etc. ...
355 views

### Scott's and Freedman–Diaconis rules of the thumb for selecting bin width - disatvantages

Scott's and Freedman–Diaconis rules of the thumb are based on the following formula: In the article here it is stated that: While these appear to be useful estimates for unimodal densities ...
86 views

### Discretize values/binning for missing data

I’m running an experiment where pairs make ratings after answering questions. There are 15 minute intervals where they attempt to answer 12 questions, but each pair makes it through a different number ...
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### Whether and how to bin data beyond a certain threshold

I've seen many admonishments against binning and agreed with them in general, but I wonder it's advisable to do so under certain circumstances. To adapt an example from my actual project, let's say ...
30 views

### Dichtomising continious treatment variables

In the past, I have repeatedly seen studies that dichtomize continuous treatment variables into a binary treatment dummy. I feel that this cannot be good practice. we loose important information we ...
72 views

### R Package for Optimal binning With Restrictions

Does anyone know about a R Package like SMBinning that can make Optimal Binning for WoE analysis where you can impose restrictions like minimum x number of Target in each bin monotome trend (WoE is ...