Mercurial > repos > bgruening > sklearn_generalized_linear
annotate README.rst @ 44:14f0ccc85505 draft
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 3c1e6c72303cfd8a5fd014734f18402b97f8ecb5
author | bgruening |
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date | Fri, 22 Sep 2023 17:41:08 +0000 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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1 Galaxy wrapper for scikit-learn library |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit cfc9fe24b7975fc5838bb3e456646202898eb977
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2 *************************************** |
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3 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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4 Contents |
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5 ======== |
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6 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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7 - `What is scikit-learn?`_ |
32a88b3bea94
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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8 - `Scikit-learn main package groups`_ |
32a88b3bea94
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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9 - `Tools offered by this wrapper`_ |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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10 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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11 - `Machine learning workflows`_ |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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12 - `Supervised learning workflows`_ |
32a88b3bea94
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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13 - `Unsupervised learning workflows`_ |
32a88b3bea94
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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14 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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15 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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16 ____________________________ |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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17 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit cfc9fe24b7975fc5838bb3e456646202898eb977
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19 .. _What is scikit-learn?: |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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20 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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21 What is scikit-learn? |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit cfc9fe24b7975fc5838bb3e456646202898eb977
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22 ===================== |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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23 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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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. |
32a88b3bea94
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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25 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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26 Scikit-learn source code can be accessed at https://github.com/scikit-learn/scikit-learn. |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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27 Detailed installation instructions can be found at http://scikit-learn.org/stable/install.html |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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28 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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29 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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30 .. _Scikit-learn main package groups: |
32a88b3bea94
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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31 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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32 Scikit-learn main package groups |
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33 ================================ |
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34 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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35 Scikit-learn provides the users with several main groups of related operations. |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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36 These are: |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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37 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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38 - Classification |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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39 - Identifying to which category an object belongs. |
32a88b3bea94
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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40 - Regression |
32a88b3bea94
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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41 - Predicting a continuous-valued attribute associated with an object. |
32a88b3bea94
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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42 - Clustering |
32a88b3bea94
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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43 - Automatic grouping of similar objects into sets. |
32a88b3bea94
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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44 - Preprocessing |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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45 - Feature extraction and normalization. |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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46 - Model selection and evaluation |
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47 - Comparing, validating and choosing parameters and models. |
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48 - Dimensionality reduction |
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49 - Reducing the number of random variables to consider. |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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50 |
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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. |
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52 |
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53 |
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54 .. _Tools offered by this wrapper: |
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55 |
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56 Available tools in the current wrapper |
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57 ====================================== |
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58 |
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59 The current release of the wrapper offers a subset of the packages from scikit-learn library. You can find: |
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60 |
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61 - A subset of classification metric functions |
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62 - Linear and quadratic discriminant classifiers |
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63 - Random forest and Ada boost classifiers and regressors |
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64 - All the clustering methods |
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65 - All support vector machine classifiers |
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66 - A subset of data preprocessing estimator classes |
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67 - Pairwise metric measurement functions |
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68 |
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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. |
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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
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70 |
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71 .. _Machine learning workflows: |
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72 |
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73 Machine learning workflows |
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74 ========================== |
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75 |
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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. |
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77 Here we discuss supervised and unsupervised learning workflows. |
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78 |
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79 .. _Supervised learning workflows: |
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80 |
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81 Supervised machine learning workflows |
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82 ===================================== |
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83 |
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84 **What is supervised learning?** |
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85 |
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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. |
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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: |
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88 |
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89 1. Preprocess the data:: |
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90 |
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91 * Change the collected data into the proper format and datatype. |
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92 * Adjust the data quality by filling the missing values, performing |
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93 required scaling and normalizations, etc. |
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94 * Extract features which are the most meaningfull for the learning task. |
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95 * Split the ready dataset into training and test samples. |
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96 |
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97 2. Choose an algorithm:: |
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98 |
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99 * These factors help one to choose a learning algorithm: |
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100 - Nature of the data (e.g. linear vs. nonlinear data) |
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101 - Structure of the predicted output (e.g. binary vs. multilabel classification) |
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102 - Memory and time usage of the training |
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103 - Predictive accuracy on new data |
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104 - Interpretability of the predictions |
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105 |
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106 3. Choose a validation method |
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107 |
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108 Every machine learning model should be evaluated before being put into practicical use. |
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109 There are numerous performance metrics to evaluate machine learning models. |
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110 For supervised learning, usually classification or regression metrics are used. |
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111 |
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112 A validation method helps to evaluate the performance metrics of a trained model in order |
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113 to optimize its performance or ultimately switch to a more efficient model. |
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114 Cross-validation is a known validation method. |
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115 |
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116 4. Fit a model |
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117 |
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118 Given the learning algorithm, validation method, and performance metric(s) |
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119 repeat the following steps:: |
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120 |
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121 * Train the model. |
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122 * Evaluate based on metrics. |
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123 * Optimize unitl satisfied. |
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124 |
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125 5. Use fitted model for prediction:: |
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126 |
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127 This is a final evaluation in which, the optimized model is used to make predictions |
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128 on unseen (here test) samples. After this, the model is put into production. |
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129 |
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130 .. _Unsupervised learning workflows: |
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131 |
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132 Unsupervised machine learning workflows |
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133 ======================================= |
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134 |
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135 **What is unsupervised learning?** |
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136 |
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137 Unlike supervised learning and more liklely in real life, here the initial data is not labeled. |
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138 The task is to extract the structure from the data and group the samples based on their similarities. |
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139 Clustering and dimensionality reduction are two famous examples of unsupervised learning tasks. |
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140 |
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141 In this case, the workflow is as follows:: |
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142 |
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143 * Preprocess the data (without splitting to train and test). |
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144 * Train a model. |
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145 * Evaluate and tune parameters. |
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146 * Analyse the model and test on real data. |