How to use the sickit-learn dataset scikit-learn is a must-have library for machine learning and data analysis. In this section, I’ll write down how to use the datasets that come with scikit-learn by default. sickit-learn Contents 1. Official datasets <= this section Create data Linear regression](/article/library/sklearn/linear_regression/) Logistic regression github Files in jupyter notebook format are here google colaboratory To run it in google colaboratory here datasets/ds_nb.
Creating a scikit-learn dataset scikit-learn provides a function to create not only the original dataset, but also the dataset itself. So, it will do the sampling for you. You can sample the dataset you need for a regression or classification problem. In most cases, the data is already given to you, but sometimes you need a simple data set for analysis, so you can use it from time to time.
Linear regression (multiple regression) of two variables with scikit-learn scikit-learn allows you to do linear regression easily, so I’ll leave this as a reminder. Here we will try to run a linear regression with two explanatory variables using scikit-learn, which is called multiple regression because it has two variables. The regression is called multiple regression because there are two variables, and single regression because there is only one explanatory variable.
Logistic regression with scikit-learn Logistic regression can be easily performed with scikit-learn, so I’ll leave it as a reminder. scikit-learn can be used for fitting and predicting. Logistic regression is called regression, but I think it is a method for solving classification problems. sickit-learn description table of contents 1. official data set creating data linear regression Logistic regression <= this section github The file in jupyter notebook format is [here](https://github.