mglearn简介
mglearn库，该库集成了sklearn和数据的许多操作方法，很便捷，获取对应数据。Helper functions for the book "Introduction to Machine Learning with Python"
GitHub：https://github.com/amueller/mglearn
1、英文解释
This repository holds the code for the forthcoming book "Introduction to Machine Learning with Python" by Andreas Muellerand Sarah Guido. You can find details about the book on the O'Reilly website.
The books requires the current stable version of scikit-learn, that is 0.20.0. Most of the book can also be used with previous versions of scikit-learn, though you need to adjust the import for everything from the model_selection module, mostly cross_val_score, train_test_split and GridSearchCV.
This repository provides the notebooks from which the book is created, together with the mglearn library of helper functions to create figures and datasets.
For the curious ones, the cover depicts a hellbender.
All datasets are included in the repository, with the exception of the aclImdb dataset, which you can download from the page of Andrew Maas. See the book for details.
If you get ImportError: No module named mglearn you can try to install mglearn into your python environment using the command pip install mglearn in your terminal or !pip install mglearn in Jupyter Notebook.
mglearn安装
pip install mglearn==0.1.6
mglearn使用方法
1、Helper functions for the book "Introduction to Machine Learning with Python"
This is the mglearn package used in the book, which you can also find in the accompanying repository
You don't need this repo, all the code is there:
https://github.com/amueller/introduction_to_ml_with_python
This repository is a stand-alone package in case you really feel like you want to install mglearn into your Python environment, for some reason or another. You can install it by running
pip install mglearn
in your terminal, or by running
!pip install mglearn
in Jupyter Notebook.
In particular, installing this package is a very easy way to add it to your Python search path. Or you can just check out the notebooks from the repository above, and add the path to the mglearn subfolder to your PYTHONPATH environment variable (or set it in your IDE).
2、Setup
To run the code, you need the packages numpy, scipy, scikit-learn, matplotlib, pandas and pillow. Some of the visualizations of decision trees and neural networks structures also require graphviz. The chapter on text processing also requirs nltk and spacy.
The easiest way to set up an environment is by installing Anaconda.
Installing packages with conda:
If you already have a Python environment set up, and you are using the conda package manager, you can get all packages by running
conda install numpy scipy scikit-learn matplotlib pandas pillow graphviz python-graphviz
For the chapter on text processing you also need to install nltk and spacy:
conda install nltk spacy
Installing packages with pip
If you already have a Python environment and are using pip to install packages, you need to run
pip install numpy scipy scikit-learn matplotlib pandas pillow graphviz
You also need to install the graphiz C-library, which is easiest using a package manager. If you are using OS X and homebrew, you can brew install graphviz. If you are on Ubuntu or debian, you can apt-get install graphviz. Installing graphviz on Windows can be tricky and using conda / anaconda is recommended. For the chapter on text processing you also need to install nltk and spacy:
pip install nltk spacy
Downloading English language model
For the text processing chapter, you need to download the English language model for spacy using
python -m spacy download en
