Mercurial > repos > bgruening > sklearn_nn_classifier

comparison README.rst @ 3:b3a37a265fa8 draft

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit cfc9fe24b7975fc5838bb3e456646202898eb977
author bgruening
date Sat, 04 Aug 2018 17:35:39 -0400
parents b7d0fc23bfe4
children
comparison
equal deleted inserted replaced
2:c4ed38067415 3:b3a37a265fa8
1 ***************
2 Galaxy wrapper for scikit-learn library 1 Galaxy wrapper for scikit-learn library
3 *************** 2 ***************************************
4 3
5 Contents 4 Contents
6 ======== 5 ========
6
7 - `What is scikit-learn?`_ 7 - `What is scikit-learn?`_
8 - `Scikit-learn main package groups`_ 8 - `Scikit-learn main package groups`_
9 - `Tools offered by this wrapper`_ 9 - `Tools offered by this wrapper`_
10 10
11 - `Machine learning workflows`_ 11 - `Machine learning workflows`_
14 14
15 15
16 ____________________________ 16 ____________________________
17 17
18 18
19 .. _What is scikit-learn? 19 .. _What is scikit-learn?:
20 20
21 What is scikit-learn? 21 What is scikit-learn?
22 =========================== 22 =====================
23 23
24 Scikit-learn is an open-source machine learning library for the Python programming language. It offers various algorithms for performing supervised and unsupervised learning as well as data preprocessing and transformation, model selection and evaluation, and dataset utilities. It is built upon SciPy (Scientific Python) library. 24 Scikit-learn is an open-source machine learning library for the Python programming language. It offers various algorithms for performing supervised and unsupervised learning as well as data preprocessing and transformation, model selection and evaluation, and dataset utilities. It is built upon SciPy (Scientific Python) library.
25 25
26 Scikit-learn source code can be accessed at https://github.com/scikit-learn/scikit-learn. 26 Scikit-learn source code can be accessed at https://github.com/scikit-learn/scikit-learn.
27 Detailed installation instructions can be found at http://scikit-learn.org/stable/install.html 27 Detailed installation instructions can be found at http://scikit-learn.org/stable/install.html
28 28
29 29
30 .. _Scikit-learn main package groups: 30 .. _Scikit-learn main package groups:
31 31
32 ======
33 Scikit-learn main package groups 32 Scikit-learn main package groups
34 ====== 33 ================================
35 34
36 Scikit-learn provides the users with several main groups of related operations. 35 Scikit-learn provides the users with several main groups of related operations.
37 These are: 36 These are:
38 37
39 - Classification 38 - Classification
52 Each group consists of a number of well-known algorithms from the category. For example, one can find hierarchical, spectral, kmeans, and other clustering methods in sklearn.cluster package. 51 Each group consists of a number of well-known algorithms from the category. For example, one can find hierarchical, spectral, kmeans, and other clustering methods in sklearn.cluster package.
53 52
54 53
55 .. _Tools offered by this wrapper: 54 .. _Tools offered by this wrapper:
56 55
57 ===================
58 Available tools in the current wrapper 56 Available tools in the current wrapper
59 =================== 57 ======================================
60 58
61 The current release of the wrapper offers a subset of the packages from scikit-learn library. You can find: 59 The current release of the wrapper offers a subset of the packages from scikit-learn library. You can find:
62 60
63 - A subset of classification metric functions 61 - A subset of classification metric functions
64 - Linear and quadratic discriminant classifiers 62 - Linear and quadratic discriminant classifiers
71 In addition, several tools for performing matrix operations, generating problem-specific datasets, and encoding text and extracting features have been prepared to help the user with more advanced operations. 69 In addition, several tools for performing matrix operations, generating problem-specific datasets, and encoding text and extracting features have been prepared to help the user with more advanced operations.
72 70
73 .. _Machine learning workflows: 71 .. _Machine learning workflows:
74 72
75 Machine learning workflows 73 Machine learning workflows
76 =============== 74 ==========================
77 75
78 Machine learning is about processes. No matter what machine learning algorithm we use, we can apply typical workflows and dataflows to produce more robust models and better predictions. 76 Machine learning is about processes. No matter what machine learning algorithm we use, we can apply typical workflows and dataflows to produce more robust models and better predictions.
79 Here we discuss supervised and unsupervised learning workflows. 77 Here we discuss supervised and unsupervised learning workflows.
80 78
81 .. _Supervised learning workflows: 79 .. _Supervised learning workflows:
82 80
83 ===================
84 Supervised machine learning workflows 81 Supervised machine learning workflows
85 =================== 82 =====================================
86 83
87 **What is supervised learning?** 84 **What is supervised learning?**
88 85
89 In this machine learning task, given sample data which are labeled, the aim is to build a model which can predict the labels for new observations. 86 In this machine learning task, given sample data which are labeled, the aim is to build a model which can predict the labels for new observations.
90 In practice, there are five steps which we can go through to start from raw input data and end up getting reasonable predictions for new samples: 87 In practice, there are five steps which we can go through to start from raw input data and end up getting reasonable predictions for new samples:
130 This is a final evaluation in which, the optimized model is used to make predictions 127 This is a final evaluation in which, the optimized model is used to make predictions
131 on unseen (here test) samples. After this, the model is put into production. 128 on unseen (here test) samples. After this, the model is put into production.
132 129
133 .. _Unsupervised learning workflows: 130 .. _Unsupervised learning workflows:
134 131
135 =======================
136 Unsupervised machine learning workflows 132 Unsupervised machine learning workflows
137 ======================= 133 =======================================
138 134
139 **What is unsupervised learning?** 135 **What is unsupervised learning?**
140 136
141 Unlike supervised learning and more liklely in real life, here the initial data is not labeled. 137 Unlike supervised learning and more liklely in real life, here the initial data is not labeled.
142 The task is to extract the structure from the data and group the samples based on their similarities. 138 The task is to extract the structure from the data and group the samples based on their similarities.