scikit-learn is used in fields such as data mining and machine learning
Contains most of the traditional machine learning methods
Come out on Google in 2006
It is based on the Python language
It is based on NumPy, SciPy, and matplotlib toolkit
There are mainly the following six functions:
Classification
Including support vector machine classification (SVC), nearest neighbors, decision tree, random forest, etc.
Regression
Including linear regression, polynomial regression, support vector regression (SVR), ridge regression, lasso regression, etc.
Clustering
k-means, spectral clustering, mean-shift and other methods
降维（Dimensionality reduction）
The effect is to reduce the dimensionality of the sample vector
For example, from 200 dimensions to 15 dimensions
Main algorithms: principal component analysis (PCA), independent component analysis (ICA) and other methods
Model selection
Role: Evaluation model, model selection, cross-validation, parameter adjustment, etc., grid search, etc.
Preprocessing
Used for data normalization, data standardization, mean removal, whitening, and binarization
In short, the data is preprocessed