https://scikit-learn.org/stable/user_guide.html

University of Michigan - Ann Arbor

Scikit-learn is an open-source machine learning library for programming languages. It provides support for  Support vector machine, random forest, gradient boosting, K-means and DBSCAN and classifications, regression and clustering algorithms, and design interoperable with numerical and scientific libraries(Numpy and Scipy). Like this, because Scikit-learn uses the scikit stack outside of the library, the biggest advantage is its compatibility with other libraries. Since the library has a unified interface, it is very simple to apply different methods.

Scikit-learn

https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html

Documentation for sklearn.neighbors.KNeighborsClassifier

Scikit-learn Cheat sheet

Scikit-learn Documentation

Typically, data that's input to scikit-learn will be in the form of a NumPy array.

NumPy vs. Scikit-learn

Some helpful sklearn functions include:

- KNeighborsClassifier

- DummyClassifier

- DummyRegressor

- LabelEncoder

- OneHotEncoder

- Train Test Split Function

- make_regression

- make_friedman1

- make_classification

- SVC

- DecisionTreeClassifier

- RandomForestClassifier

- RandomForestRegressor

- GridSearchCV


Helpful Scikit-Learn functions

Whether on Mac or Windows, go to the command line and type:
```terminal
pip install -U scikit-learn
```

Learn Before

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