Automize machine learning using ChatGPT
Before we start
chatgptAutostats package does a lot of stuff for data scientists like automization of Multi-linear Regression (MLR) analysis and many more.
Introduction
The world of machine learning and artificial intelligence is constantly evolving, and the ChatGPT is one of the latest developments in this field. ChatGPT is a large language model trained by OpenAI, based on the GPT-3.5 architecture. This model has shown great promise in natural language processing and generating realistic text. Recently, the developers have released a new package called “chatgptAutostats,” which aims to provide tools for model explainability and fitting domain-expert models.
The chatgptAutostats package can be installed in R programming language using the following commands:
devtools::install_github("ben-aaron188/rgpt3")
devtools::install_github("arkraieski/chatgptAutostats")
It is important to note that this package is still a work in progress and may change rapidly or even break. The developers encourage users to use the package responsibly and not as a “weapon of math destruction.”
The primary goal of this package is to provide tools for model explainability and fitting domain-expert models. The package will initially focus on linear and logistic regression models. The developers aim to make it easier for users to understand how their models are making predictions and to detect potential biases or issues with the models.
The package includes a built-in dataset called “swiss” that can be used to demonstrate its functionality. The “rgpt3” package needs to be installed and authenticated using the access key file before using chatgptAutostats. After that, users can load the chatgptAutostats library and use the “chatgpt_lm” function to fit a linear regression model. The “predict_col” parameter specifies the column to predict, and the “df” parameter specifies the dataset to use.
rgpt3::gpt3_authenticate("PATHTO/access_key.txt")
library(chatgptAutostats)
my_model <- chatgpt_lm(df = swiss, predict_col = "Examination")
summary(my_model)
Advantages
- The chatgptAutostats package offers tools for model explainability and fitting domain-expert models
- Built-in datasets such as “swiss” can be used to fit linear regression models and predict the values of a specified column
- The resulting “my_model” object is a normal R “lm” class object that can be analyzed and visualized using normal S3 methods
- The “summary” function can be used to obtain information about the fitted model, such as the coefficients, standard errors, t-statistics, and p-values
- These outputs can help users better understand how their models are making predictions and detect potential biases or issues
- The chatgptAutostats package is a valuable addition to the machine learning toolbox
- Users must use the package responsibly and not as a “weapon of math destruction”
The resulting “my_model” object is a normal R “lm” class object, so users can use the normal S3 methods to analyze and visualize the model.
Overall, the chatgptAutostats package is a promising development in the field of model explainability and domain-expert models. It provides tools for users to better understand their models and detect potential biases or issues. However, users should use the package responsibly and not use it as a “weapon of math destruction.”
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