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annotate svm_classifier.xml @ 2:caba07f41453 draft default tip

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"planemo upload for repository https://github.com/secimTools/SECIMTools/tree/main/galaxy commit 498abad641099412df56f04ff6e144e4193bbc34-dirty"
author malex
date Thu, 10 Jun 2021 15:41:17 +0000
parents 2e7d47c0b027
children
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2e7d47c0b027 "planemo upload for repository https://malex@toolshed.g2.bx.psu.edu/repos/malex/secimtools"
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1 <tool id="secimtools_svm_classifier" name="Support Vector Machine (SVM) Classifier" version="@WRAPPER_VERSION@">
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2 <description>- Predict sample groups.</description>
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3 <macros>
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4 <import>macros.xml</import>
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5 </macros>
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6 <expand macro="requirements" />
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7 <command><![CDATA[
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8 svm_classifier.py
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9 --train_wide $train_wide
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10 --train_design $train_design
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11 --test_wide $test_wide
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12 --test_design $test_design
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13 --group $group
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14 --ID $uniqID
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15 --kernel $kernel
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16 --degree $degree
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17 --C $C
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18 --cross_validation $cross_validation
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19 --C_lower_bound $C_lower_bound
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20 --C_upper_bound $C_upper_bound
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21 --a $a
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22 --b $b
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23 --outClassification $outClassification
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24 --outClassificationAccuracy $outClassificationAccuracy
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25 --outPrediction $outPrediction
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26 --outPredictionAccuracy $outPredictionAccuracy
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27 ]]></command>
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28 <inputs>
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29 <param name="train_wide" type="data" format="tabular" label="Training wide dataset" help="Dataset missing? See TIP below."/>
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30 <param name="train_design" type="data" format="tabular" label="Training design file" help="Dataset missing? See TIP below."/>
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31 <param name="test_wide" type="data" format="tabular" label="Target wide dataset" help="Dataset missing? See TIP below."/>
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32 <param name="test_design" type="data" format="tabular" label="Target design file" help="Dataset missing? See TIP below."/>
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33 <param name="group" size="30" type="text" value="" label="Group/Treatment" help="Name of the column in your Training and Target design files that contain group classifications."/>
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34 <param name="uniqID" type="text" size="30" value="" label="Unique Feature ID" help="Name of the column in your Training and Target wide datasets that contain unique identifiers."/>
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35 <param name="kernel" type="select" size="30" display="radio" value="rbf" label="Select a SVM Kernel Function">
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36 <option value="rbf">Radial Basis function (Gaussian)</option>
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37 <option value="linear">Linear</option>
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38 <option value="poly">Polynomial</option>
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39 <option value="sigmoid">Sigmoid</option>
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40 </param>
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41 <param name="degree" size="30" type="text" value="3" label="Polynomial Degree" help='Only used for the polynomial kernel.'/>
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42 <param name="cross_validation" type="select" size="30" display="radio" value="double" label="Select Cross-Validation">
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43 <option value="none">None</option>
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44 <option value="single">Single</option>
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45 <option value="double">Double</option>
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46 </param>
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47 <param name="C" size="30" type="text" value="1" label="Regularization Parameter C" help='See references in tool description for setting this parameter. Value must be positive (C > 0). Used only if cross-validation is not selected. Default = 1.'/>
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48 <param name="C_lower_bound" size="30" type="text" value="0.1" label="Regularization Parameter C (Lower Bound)" help='Defines the lower bound for regularization parameter C when cross-validation is used. Must have a positive value (C > 0) Default = 0.1. '/>
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49 <param name="C_upper_bound" size="30" type="text" value="10" label="Regularization Parameter C (Upper Bound)" help='Defines the upper bound for regularization parameter C when cross-validation is used. Must have a positive value that is larger than the lower bound. Default = 10. '/>
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50 <param name="a" size="30" type="text" value="0.0" label="Coefficient A" help='Used in the kernel functions above. Must be greater than zero. However, the default = 0 and translates to a = 1/n_features, where n_features is the number of features. Default = 0.'/>
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51 <param name="b" size="30" type="text" value="0.0" label="Coefficient B" help='Independent term in kernel function. It is only significant in polynomial and sigmoid kernels. Default = 0.'/>
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52 </inputs>
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53 <outputs>
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54 <data name="outClassification" format="tabular" label="${tool.name} on ${on_string}: Classification of the Training Data Set"/>
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55 <data name="outClassificationAccuracy" format='tabular' label="${tool.name} on ${on_string}: Classification Accuracy of the Training Data Set"/>
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56 <data name="outPrediction" format="tabular" label="${tool.name} on ${on_string}: Prediction Accuracy of the Target Data Set"/>
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57 <data name="outPredictionAccuracy" format='tabular' label="${tool.name} on ${on_string}: Prediction Accuracy of the Target Data Set"/>
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58 </outputs>
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59 <tests>
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60 <test>
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61 <param name="train_wide" value="ST000006_data.tsv"/>
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62 <param name="train_design" value="ST000006_design.tsv"/>
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63 <param name="test_wide" value="ST000006_data.tsv"/>
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64 <param name="test_design" value="ST000006_design.tsv"/>
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65 <param name="group" value="White_wine_type_and_source" />
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66 <param name="uniqID" value="Retention_Index" />
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67 <param name="kernel" value="linear"/>
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68 <param name="degree" value="3"/>
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69 <param name="cross_validation" value="none"/>
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70 <param name="C" value="1"/>
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71 <param name="C_lower_bound" value="0.1"/>
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72 <param name="C_upper_bound" value="2"/>
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73 <param name="a" value="1"/>
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74 <param name="b" value="1"/>
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75 <output name="outClassification" file="ST000006_svm_classifier_train_classification.tsv" />
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76 <output name="outClassificationAccuracy" file="ST000006_svm_classifier_train_classification_accuracy.tsv" />
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77 <output name="outPrediction" file="ST000006_svm_classifier_target_classification.tsv" />
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78 <output name="outPredictionAccuracy" file="ST000006_svm_classifier_target_classification_accuracy.tsv" />
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79 </test>
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80 </tests>
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81 <help><![CDATA[
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82
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83 **TIP:**
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84 If your data is not TAB delimited, use *Text Manipulation-&gt;Convert*.
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85
2e7d47c0b027 "planemo upload for repository https://malex@toolshed.g2.bx.psu.edu/repos/malex/secimtools"
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86 **WARNINGS:**
2e7d47c0b027 "planemo upload for repository https://malex@toolshed.g2.bx.psu.edu/repos/malex/secimtools"
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87 - (1) This script automatically removes spaces and special characters from strings.
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88 - (2) If a feature name starts with a number it will prepend an '_'.
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89
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90 **Tool Description**
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91
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92 **NOTE: A minimum of 100 samples is required by the tool for single or double cross validation**
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93
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94 Given a set of supervised samples in a Training Dataset, the SVM training algorithm builds a model based on these samples that can be used for predicting the categories of new, unclassified samples in a Target Dataset.
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95 The Target Dataset is not used for model training or evaluation, only for prediction based on the finalized model.
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96 SVM classification is performed on the target data and accuracy is estimated for both Target and Training Datasets.
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97
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98 SVM uses different kernel functions to carry out different types of classification such as radial bassis (gaussian), linear, polynomial, and sigmoid.
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99 The classification model can be trained with and without cross-validation (single or double).
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100
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101 For single and double cross-validation: the training dataset is split differently when the model fit is performed.
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102
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103 In single cross-validation: the same data are used to both fit and evaluate the model.
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104
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105 In double cross-validation: the training dataset is split into pieces and the model fit is performed on one of the pieces and evaluated on the other pieces.
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106
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107 Under cross-validation, the user specifies Regularization Parameter C and the Upper and Lower bounds of Regularization Parameter C.
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108 For more information about Regularization Parameter C, see references below:
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109
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110 Cortes, C. and Vapnik, V. 1995. Support-vector networks. Machine Learning. 20(3) 273-297.
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111
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112 Steinwart, I and Christmann, A. 2008. Support vector machines. Springer Science and Business Media.
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113
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114
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115 To use the SVM tool, users need the following information:
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116
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117 (i) a Training Dataset with known categories in the training design file and
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118 (ii) a Target Dataset with predicted categories in the target design file.
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119 (iii) the name of the Group/Treatment classification column should be the same for both design files.
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120 (iv) the Unique Feature IDs should be the same in both the wide datasets.
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121 (v) the number of Unique Feature IDs should be the same in both the wide datasets.
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122
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123 ------------------------------------------------------------------------------
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124
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125 **Input**
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126
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127 - Four input datasets are required.
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128
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129 @WIDE@
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130
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131 **NOTE:** The sample IDs must match the sample IDs in the Design File
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132 (below). Extra columns will automatically be ignored.
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133
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134 @METADATA@
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135
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136 @GROUP@
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137
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138 @UNIQID@
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139
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140 **SVM Kernel Function**
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141
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142 - Kernel functions available for the SVM algorithm.
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143
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144 **Polynomial Degree**
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145
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146 - Only used for the polynomial kernel.
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147
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148 **Cross-Validation Choice**
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149
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150 - Cross-validation options available for the user. 'None' corresponds to no cross-validation- the user specifies regularization parameter C manually.
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151
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152
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153 **Regularization Parameter C**
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154
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155 - Penalizes potential overfitting, must be positive.
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156
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157
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158 **Regularization Parameter C (Lower Bound)**
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159
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160 - Lower bound for regularization parameter C. Value must be greater than 0. Only if cross-validation is selected.
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161
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162
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163 **Regularization Parameter C (Upper Bound)**
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164
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165 - Upper bound for regularization parameter C. Value must be greater than the Lower Bound.
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166
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167
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168 **Coefficient A**
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169
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170 - Used in the kernel functions above. Must be greater than zero. Default = 0, however,
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171 this translates to a = 1/n_features, where n_features is the number of features.
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172
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173 **Coefficent B**
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174
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175 - Independent term in the kernel function. It is only significant in
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176 polynomial and sigmoid kernels.
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177
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178 ------------------------------------------------------------------------------
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179
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180 **Output**
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181
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182 This tool will output two files for the Training dataset and two for the Target datset:
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183
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184 Training:
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185
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186 (1) a TSV file containing the observed and predicted grouping classifications for each sample and
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187 (2) a TSV file containing the accuracy (percentage) of the classification.
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188
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189 Target:
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190
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191 (3) a TSV file containing suspected and predicted grouping classifications for each sample and
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192 (4) a TSV file containing the accuracy (percentage) of the prediction in comparison to the suspected grouping specified in the design file.
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193
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194 **NOTE:** Some knowledge about the SVM classifier algorithm and different kernel types is recommended for users who plan to use the tool frequently with different settings.
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195
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196 ]]></help>
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197 <expand macro="citations"/>
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198 </tool>