Cumulative Distribution Function

Cumulative Distribution Function

Cumulative Distribution Function

Description

The Cumulative Distribution Function (CDF) helps to learn about the distribution of data by calculating the cumulative probability of a given variable value in a dataset.

Why to use

To calculate the probability of a given variable value by trying to fit it with different distribution functions.

When to use

For numerical variables having positive or negative values.

When not to use

Prerequisites

The data should not contain any missing values.

Input

Numerical variable having any positive or negative value.

Output

  • CDF curve
  • Cumulative Probability Function predicted by CDF

Statistical Methods used

Cumulative Probability

Limitations

It can be used only on numerical data.


CDF takes input as a random variable value (either discrete or continuous) and then determines the cumulative probability of that variable. By default, the data is sorted and then sent to the algorithm. Also, in the output Data tab, the resultant data appears in a sorted manner.
    • Related Articles

    • Inverse Cumulative Distribution Function

      Inverse Cumulative Distribution Function Description Inverse Cumulative Distribution Function (ICDF) is the inverse of Cumulative Distribution Function (CDF). It helps to learn about the distribution of data by calculating the value associated with a ...
    • Parametric Distribution Fitting

      Parametric Distribution Fitting Description Parametric distribution fitting is the process used to select a statistical distribution that best fits a data set. Why to use Statistical Analysis When to use To decide the distribution best suited for ...
    • Rubiscape Spring '22

      New Features Platform & Studio Rubiscape Persistent variables in workflow and workbook - The user can declare a variable to be remembered between function calls Separate Service for Visualization - Provide separate service for Visualization which ...
    • Gradient Boosting in Classification

      Gradient Boosting in Classification Description Gradient boosting is a machine learning algorithm. It is a learning method that combines multiple predictive models like decision tree to create a strong predictive model. Why use High Predictive ...
    • Statistical Concepts

      Accuracy Accuracy (of classification) of a predictive model is the ratio of the total number of correct predictions made by the model to the total predictions made. Thus, Accuracy = (TP + TN) / (TP + TN + FP + FN) Where, TP, TN, FP, and FN indicate ...