What is the Jarque Bera test in ML Python?

Learn about the Jarque Bera test in Python for machine learning applications, examining data normality. | ProjectPro

Suppose you are working on a machine learning project for stock price prediction and analyzing a particular stock's historical daily returns over the past year. You want to assess whether these returns are normally distributed, as many statistical models used in stock price prediction assume a normal distribution of returns. In such circumstances, you can collect the daily returns data and conduct a Jarque-Bera test.

The Jarque Bera test is a valuable tool for assessing the normality of data distributions. This tutorial will cover its intricacies, its significance in machine learning, and how to interpret its results effectively.

What is the Jarque Bera Test?

The Jarque-Bera test tests the goodness of data fitting, whether the data have skewness and kurtosis that match a normal distribution curve. To conduct the Jarque-Bera test, we directly use the inbuilt jarque_bera() function, which is available in the sci-py library.

It is a statistical test to determine whether a given dataset follows a normal distribution. It evaluates whether the data's skewness and kurtosis match a normal distribution. Named after Carlos Jarque and Anil K. Bera, this test is widely employed in various fields, including finance, economics, and machine learning. 

Why is the Jarque Bera Test Important in Machine Learning?

The following are the reasons that help you understand the importance of the Jarque Bera Test in Machine Learning - 

  • Data Preprocessing: Before applying specific machine learning algorithms that assume normality, such as linear regression, ensuring that the data adheres to a normal distribution is crucial.

  • Assumption Checking: Validating the normality assumption is fundamental in statistical modeling. Incorrect assumptions can lead to biased estimates and inaccurate predictions.

  • Feature Engineering: Understanding the distribution of features can aid in feature engineering, helping to create more robust and accurate models.

How to Implement the Jarque Bera Test in Python? 

The scipy.stats module in Python provides a convenient way to perform the Jarque Bera test. Here's a basic implementation:

Jarque Bera Test Implementation in Python

Example - Jarque Bera Test in Python

Step 1- Importing Libraries.

import numpy as np

import scipy.stats as stats

import pandas as pd

Step 2- Reading File.

df= pd.read_csv('/content/sample_data/california_housing_train.csv')

df.head()

Step 3- Applying jarque_bera test.

#perform Jarque-Bera test

stats.jarque_bera(df)

The test statistic is 2009089.7744870293, and the corresponding p-value is 0.0. The p-value is less than 0.05, we reject the null hypothesis. Now we have sufficient evidence to say that this data has skewness and kurtosis, which is different from a normal distribution.

How to Implement the Jarque Bera Test in R? 

You can also implement the Jarque Bera Test in R language using the jarque.test() function from the tseries package. Check out the excellent example below - 

Jarque Bera Test in R- Example

The above code generates a vector of 100 random normal-distributed numbers and then performs the Jarque-Bera Test on this data using the jarque.test(). This function returns a test statistic and p-value. You can further interpret the result based on the significance level you choose. If the p-value is less than the chosen significance level (e.g., 0.05), you reject the null hypothesis that the data is normally distributed. Otherwise, you fail to reject the null hypothesis. 

Interpreting the Results: Jarque Bera Test 

  • Jarque Bera Statistic (JB): This value represents the test statistic calculated by the Jarque Bera test. Higher values indicate a more significant deviation from normality.

  • P-value: The p-value associated with the test. It indicates the probability of observing the test statistic, assuming the data is usually distributed. A lower p-value suggests more substantial evidence against the null hypothesis (i.e., the data follows a normal distribution).

  • Threshold: Typically, a significance level of 0.05 is used. If the p-value is less than this threshold, we reject the null hypothesis and conclude that the data does not follow a normal distribution.

Learn more about the Jarque Bera Test with ProjectPro! 

The Jarque Bera test is crucial in machine learning for assessing data normality and aiding decisions in preprocessing, assumption validation, and feature engineering. Incorporating this test enhances model reliability and accuracy. ProjectPro offers over 270+ data science and big data projects, providing hands-on learning opportunities with ML projects that can help you leverage the implementation of the Jarque Bera test. So, what are you waiting for? Start your journey with ProjectPro today and unlock the door to boundless possibilities in data science and big data.

What Users are saying..

profile image

Savvy Sahai

Data Science Intern, Capgemini
linkedin profile url

As a student looking to break into the field of data engineering and data science, one can get really confused as to which path to take. Very few ways to do it are Google, YouTube, etc. I was one of... Read More

Relevant Projects

Avocado Machine Learning Project Python for Price Prediction
In this ML Project, you will use the Avocado dataset to build a machine learning model to predict the average price of avocado which is continuous in nature based on region and varieties of avocado.

Azure Deep Learning-Deploy RNN CNN models for TimeSeries
In this Azure MLOps Project, you will learn to perform docker-based deployment of RNN and CNN Models for Time Series Forecasting on Azure Cloud.

Walmart Sales Forecasting Data Science Project
Data Science Project in R-Predict the sales for each department using historical markdown data from the Walmart dataset containing data of 45 Walmart stores.

AWS MLOps Project to Deploy a Classification Model [Banking]
In this AWS MLOps project, you will learn how to deploy a classification model using Flask on AWS.

Image Classification Model using Transfer Learning in PyTorch
In this PyTorch Project, you will build an image classification model in PyTorch using the ResNet pre-trained model.

Build a Customer Churn Prediction Model using Decision Trees
Develop a customer churn prediction model using decision tree machine learning algorithms and data science on streaming service data.

Langchain Project for Customer Support App in Python
In this LLM Project, you will learn how to enhance customer support interactions through Large Language Models (LLMs), enabling intelligent, context-aware responses. This Langchain project aims to seamlessly integrate LLM technology with databases, PDF knowledge bases, and audio processing agents to create a comprehensive customer support application.

Natural language processing Chatbot application using NLTK for text classification
In this NLP AI application, we build the core conversational engine for a chatbot. We use the popular NLTK text classification library to achieve this.

Build Regression (Linear,Ridge,Lasso) Models in NumPy Python
In this machine learning regression project, you will learn to build NumPy Regression Models (Linear Regression, Ridge Regression, Lasso Regression) from Scratch.

Deploying Machine Learning Models with Flask for Beginners
In this MLOps on GCP project you will learn to deploy a sales forecasting ML Model using Flask.