What is Kernal density estimation in the StatsModels library?

This recipe describes what is Kernal density estimation in the StatsModels library

Recipe Objective - What is Kernal density estimation in the StatsModels library?

The functionality of kernel density estimation (KDE) is divided into univariate and multivariate estimates, which are implemented in very different ways. The
Univariate estimation (provided by KDEUnivariate) is very fast because it uses the FFT transform. Therefore, it is suitable for continuous univariate data when speed is important. Supports the use of different kernels. Bandwidth estimates are based solely on heuristics (Scott or Silverman).
Multivariate estimation (provided by KDEMultivariate) uses the product kernel. Supports least squares and maximum likelihood cross-validation for bandwidth estimation and mixed data estimation of continuous, ordering, and disorder. Currently, the standard kernels (Gaussian, WangRyzin, and AitchisonAitken) cannot be modified. Direct estimation of conditional density is supported by KDEMultivariateConditional. The
KDE Multivariate can also perform univariate estimates but is up to two orders of magnitude slower than the KDE Univariate.

 

List of Classification Algorithms in Machine Learning  

For more related projects -

https://www.dezyre.com/projects/data-science-projects/deep-learning-projects
https://www.dezyre.com/projects/data-science-projects/neural-network-projects

KDEUnivariate:

Univariate Kernel Density Estimator

Parameter:

endog => The variable for which the density estimate is desired.

KDEMultivariate:

Multivariate kernel density estimator.

This density estimator can handle both univariate and multivariate data, including mixed continuous / ordered discrete / unordered discrete data. It also provides a mutually validated bandwidth selection method (least squares, maximum likelihood method).

Parameters:

data => training data

var_type => type of variables(c : continuous, u : unordered (discrete),o : ordered (discrete))

What Users are saying..

profile image

Jingwei Li

Graduate Research assistance at Stony Brook University
linkedin profile url

ProjectPro is an awesome platform that helps me learn much hands-on industrial experience with a step-by-step walkthrough of projects. There are two primary paths to learn: Data Science and Big Data.... Read More

Relevant Projects

Build Regression Models in Python for House Price Prediction
In this Machine Learning Regression project, you will build and evaluate various regression models in Python for house price prediction.

Build Deep Autoencoders Model for Anomaly Detection in Python
In this deep learning project , you will build and deploy a deep autoencoders model using Flask.

Skip Gram Model Python Implementation for Word Embeddings
Skip-Gram Model word2vec Example -Learn how to implement the skip gram algorithm in NLP for word embeddings on a set of documents.

Learn to Build an End-to-End Machine Learning Pipeline - Part 2
In this Machine Learning Project, you will learn how to build an end-to-end machine learning pipeline for predicting truck delays, incorporating Hopsworks' feature store and Weights and Biases for model experimentation.

Build a Text Generator Model using Amazon SageMaker
In this Deep Learning Project, you will train a Text Generator Model on Amazon Reviews Dataset using LSTM Algorithm in PyTorch and deploy it on Amazon SageMaker.

Learn How to Build a Linear Regression Model in PyTorch
In this Machine Learning Project, you will learn how to build a simple linear regression model in PyTorch to predict the number of days subscribed.

AWS MLOps Project for ARCH and GARCH Time Series Models
Build and deploy ARCH and GARCH time series forecasting models in Python on AWS .

Demand prediction of driver availability using multistep time series analysis
In this supervised learning machine learning project, you will predict the availability of a driver in a specific area by using multi step time series analysis.

Linear Regression Model Project in Python for Beginners Part 1
Machine Learning Linear Regression Project in Python to build a simple linear regression model and master the fundamentals of regression for beginners.

Time Series Project to Build a Multiple Linear Regression Model
Learn to build a Multiple linear regression model in Python on Time Series Data