Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and so on.

In this page, we write some Scikit-learn tutorials and examples for show you how to use Scikit-learn correctly.

In python, we can implement K-Means clustering by using sklearn.cluster.KMeans easily. In this tutorial, we will use some examples to show you how to do.

In order to evaluate the performance of a classification model, we have to draw a roc curve based on fpr and tpr. In this tutorial, we will introduce you how to do.

sklearn.metrics.roc_curve() can allow us to compute receiver operating characteristic (ROC) easily. In this tutorial, we will use some examples to show you how to use it.

sklearn.model_selection.train_test_split() function allows us to split a data set to train set and test set easily. In this tutorial, we will use an example to show you how to use it correctly.

Ordinary Least Squares is a simple linear model in scikit-learn, in this tutorial, we will write an example to explain how to implement ordinary least squares linear regression for beginners.

Ordinary Least Squares is a kind of linear regression models. It is simple and easy to understand. In this tutorial, we will explain it for you to help you understand it.

R2 coefficient is often used to evaluate the effect of predicting the targets. In this tutorial, we will introduce some its basic information to help you understand and use it in machine learning.