How can latent variables simplify exploratory data analysis?

由人工智能和领英社区提供技术支持

Exploratory data analysis (EDA) is a crucial step in any data analytics project, as it helps you understand the structure, patterns, and relationships in your data. However, EDA can also be challenging and time-consuming, especially when you have a large number of variables to examine. How can you reduce the complexity and dimensionality of your data without losing important information? One possible solution is to use latent variables.

此文章中的业界达人

由社区从 10 条内容中精选。了解更多

Ashish Singh

Visionary Senior Director Data Engineering | Data Analytics | Data Governance | Paving path for AI & ML | Speaker | Ex…
Juliane Parker

Data Analyst with a passion for health equity.

1 What are latent variables?

Latent variables are unobserved or hidden variables that cannot be measured directly, but can be inferred from other observable variables. For example, customer satisfaction is a latent variable that can be estimated from survey responses, ratings, or feedback. Latent variables can capture the underlying factors, concepts, or themes that influence or explain the behavior of the observed variables. For example, personality traits, attitudes, or skills are latent variables that can affect how people perform on different tasks or tests.

添加您的观点

Ashish Singh

Visionary Senior Director Data Engineering | Data Analytics | Data Governance | Paving path for AI & ML | Speaker | Ex Yahoo, Credit Suisse, UBS
举报内容
Latent variables are unobservable factors inferred from observable data. They encapsulate underlying concepts influencing observed variables. For instance, customer satisfaction inferred from survey responses is a latent variable. They simplify exploratory data analysis by revealing hidden patterns, reducing complexity. Techniques like factor analysis or latent class analysis extract latent variables, aiding in dimensionality reduction and uncovering meaningful relationships beyond surface data. Understanding latent variables enhances insights into complex datasets, guiding effective decision-making and predictive modeling.

已翻译

赞
Wilfredo Alpirez

Senior Data Engineer |Data Scientist|AWS|Azure|Backend Engineer|Python|Snowflake|SQL|LLMS|Data Driven|
举报内容
Las variables latentes son aquellas que no se pueden observar directamente pero se pueden deducir a partir de otras variables. Por ejemplo, la satisfacción del cliente se infiere a partir de encuestas o calificaciones. Capturan factores subyacentes que influyen en variables observables, como rasgos de personalidad o actitudes, que pueden afectar el comportamiento.

已翻译

赞

加载更多内容

2 How can latent variables simplify EDA?

By using latent variables, you can simplify EDA in two ways. First, you can reduce the number of variables you need to analyze, by grouping them into a smaller set of latent variables that represent the main dimensions or sources of variation in your data. This can help you identify the most relevant and meaningful factors that describe your data, and avoid redundancy or noise. Second, you can visualize and interpret your data more easily, by plotting or summarizing the latent variables instead of the original variables. This can help you discover patterns, trends, or outliers that might not be apparent in the raw data.

添加您的观点

Juliane Parker

Data Analyst with a passion for health equity.
举报内容
Latent variables also help keep your EDA explainable and relevant. The distribution of many data points (or any other EDA step) often has very little real world meaning, making cleanup efforts difficult. Latent variables can add that meaning and make the entire EDA / cleanup process much easier for business stakeholders to see the value of EDA and data cleaning work.

已翻译

赞
Ashish Singh

Visionary Senior Director Data Engineering | Data Analytics | Data Governance | Paving path for AI & ML | Speaker | Ex Yahoo, Credit Suisse, UBS
举报内容
Latent variables simplify exploratory data analysis (EDA) by reducing the number of variables, grouping them into a smaller set that represents main dimensions or sources of variation. This aids in identifying relevant factors and avoiding redundancy. Additionally, visualizing and interpreting latent variables instead of original ones enables easier discovery of patterns and outliers in the data. Leveraging techniques like principal component analysis (PCA) or factor analysis facilitates the extraction of latent variables, streamlining EDA and enhancing insights.

已翻译

赞
Wilfredo Alpirez

Senior Data Engineer |Data Scientist|AWS|Azure|Backend Engineer|Python|Snowflake|SQL|LLMS|Data Driven|
举报内容
El uso de variables latentes puede simplificar la EDA de dos formas. Primero, reduce el número de variables al agruparlas en un conjunto más peque?o que represente las principales dimensiones de los datos. Esto identifica los factores más relevantes y evita la redundancia. Segundo, al visualizar e interpretar estas variables en lugar de las originales, facilita la detección de patrones o valores atípicos.

已翻译

赞

3 How can you find latent variables?

One common method to find latent variables is factor analysis, a statistical technique that aims to explain the correlations among a large set of observed variables by a smaller set of latent variables, called factors. Factor analysis assumes that each observed variable is a linear combination of some factors, plus some error term. The factors are not directly observable, but can be estimated from the data using various algorithms and criteria. Factor analysis can help you answer questions such as: How many factors are there in your data? What do the factors represent? How are the factors related to the observed variables?

添加您的观点

Ashish Singh

Visionary Senior Director Data Engineering | Data Analytics | Data Governance | Paving path for AI & ML | Speaker | Ex Yahoo, Credit Suisse, UBS
举报内容
To find latent variables, utilize factor analysis, a statistical technique explaining correlations among observed variables using a smaller set of latent variables, or factors. These factors are not directly observable but can be estimated from data using algorithms. Factor analysis aids in determining the number of factors, their representations, and their relationship with observed variables. Latent variables simplify exploratory data analysis by reducing complexity, revealing underlying patterns, and facilitating interpretation, leading to more concise insights and informed decision-making.

已翻译

赞
Wilfredo Alpirez

Senior Data Engineer |Data Scientist|AWS|Azure|Backend Engineer|Python|Snowflake|SQL|LLMS|Data Driven|
举报内容
Un método común para encontrar variables latentes es el análisis factorial. Esta técnica estadística busca explicar las correlaciones entre un conjunto amplio de variables observadas mediante un conjunto más reducido de variables latentes llamadas factores. Se asume que cada variable observada es una combinación lineal de estos factores más un término de error. Los factores, aunque no son directamente observables, pueden estimarse a partir de los datos utilizando diversos algoritmos y criterios. El análisis factorial ayuda a determinar el número de factores en los datos, lo que representan y cómo se relacionan con las variables observadas.

已翻译

赞

4 How can you use factor analysis in EDA?

To use factor analysis in EDA, you need to follow some steps. First, you need to prepare your data by checking its suitability for factor analysis, such as the sample size, the variable types, the distribution, and the correlation matrix. Second, you need to choose the type of factor analysis, such as exploratory or confirmatory, and the extraction and rotation methods, such as principal component analysis or varimax. Third, you need to determine the number of factors to retain, based on criteria such as eigenvalues, scree plot, or variance explained. Fourth, you need to interpret the factors by looking at the factor loadings, which indicate how strongly each observed variable is associated with each factor. Fifth, you need to validate and refine your factor model by assessing its fit, reliability, and validity.

添加您的观点

Ashish Singh

Visionary Senior Director Data Engineering | Data Analytics | Data Governance | Paving path for AI & ML | Speaker | Ex Yahoo, Credit Suisse, UBS
举报内容
Factor analysis in EDA simplifies analysis by identifying latent variables impacting observed variables. Prepare data by checking suitability. Choose factor analysis type, extraction, and rotation methods. Determine factors to retain based on criteria like eigenvalues. Interpret factors using factor loadings. Validate and refine the model for fit, reliability, and validity. Latent variables streamline EDA by uncovering underlying patterns, reducing complexity, and aiding in feature selection and dimensionality reduction.

已翻译

赞
Wilfredo Alpirez

Senior Data Engineer |Data Scientist|AWS|Azure|Backend Engineer|Python|Snowflake|SQL|LLMS|Data Driven|
举报内容
Para utilizar el análisis factorial en EDA, siga estos pasos: 1. Prepare los datos verificando su idoneidad, como el tama?o de la muestra y la distribución. 2. Elija el tipo de análisis y los métodos de extracción y rotación. 3. Determine el número de factores a conservar utilizando criterios como los valores propios. 4. Interprete los factores observando las cargas factoriales, que indican la asociación entre variables observadas y factores. 5. Valide y refine su modelo factorial evaluando su ajuste, confiabilidad y validez.

已翻译

赞

5 How can you perform factor analysis in R?

To perform factor analysis in R, you can use the factanal function from the base package, or the fa function from the psych package. Both functions allow you to specify the number of factors, the extraction and rotation methods, and the output options. For example, to perform a factor analysis on the USArrests data set, which contains the rates of arrests for murder, assault, and rape, and the percentage of urban population for each state in the US, you can use the following code:

# Load the data
data(USArrests)
# Perform factor analysis with two factors and varimax rotation
fa_result <- fa(USArrests, nfactors = 2, rotate = "varimax")
# Print the summary of the result
summary(fa_result)

The output will show you the factor loadings, the variance explained, the factor scores, and some statistics to evaluate the model. You can also use the plot function to visualize the factor loadings or the factor scores on a scatter plot.

添加您的观点

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

添加您的观点

Data Analytics

+ 关注

给文章评分

我们借助人工智能创建了此文章。您认为这篇文章怎么样？

很棒不太好

举报此文章

How can latent variables simplify exploratory data analysis?

1

2

3

4

5

6

1 What are latent variables?

2 How can latent variables simplify EDA?

3 How can you find latent variables?

4 How can you use factor analysis in EDA?

5 How can you perform factor analysis in R?

6 Here’s what else to consider

Data Analytics

给文章评分

感谢您的反馈

更多Data Analytics相关文章

更多相关阅读内容