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annotate svm.xml @ 13:78c664cc1841 draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5d71c93a3dd804b1469852240a86021ab9130364
author bgruening
date Mon, 09 Jul 2018 14:31:08 -0400
parents 9a9396e5d153
children eaccbf2c2891
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1 <tool id="svm_classifier" name="Support vector machines (SVMs)" version="@VERSION@">
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2 <description>for classification</description>
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3 <macros>
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4 <import>main_macros.xml</import>
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5 <!-- macro name="class_weight" argument="class_weight"-->
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6 </macros>
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7 <expand macro="python_requirements"/>
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8 <expand macro="macro_stdio"/>
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9 <version_command>echo "@VERSION@"</version_command>
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10 <command><![CDATA[
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11 python "$svc_script" '$inputs'
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12 ]]>
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13 </command>
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14 <configfiles>
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15 <inputs name="inputs"/>
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16 <configfile name="svc_script">
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17 <![CDATA[
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18 import sys
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19 import json
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20 import numpy as np
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21 import sklearn.svm
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22 import pandas
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23 import pickle
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25 @COLUMNS_FUNCTION@
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26 @GET_X_y_FUNCTION@
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28 input_json_path = sys.argv[1]
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29 params = json.load(open(input_json_path, "r"))
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31 #if $selected_tasks.selected_task == "load":
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33 classifier_object = pickle.load(open("$infile_model", 'rb'))
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34
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35 header = 'infer' if params["selected_tasks"]["header"] else None
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36 data = pandas.read_csv("$selected_tasks.infile_data", sep='\t', header=header, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False)
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37 prediction = classifier_object.predict(data)
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38 prediction_df = pandas.DataFrame(prediction)
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39 res = pandas.concat([data, prediction_df], axis=1)
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40 res.to_csv(path_or_buf = "$outfile_predict", sep="\t", index=False)
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42 #else:
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44 X, y = get_X_y(params, "$selected_tasks.selected_algorithms.input_options.infile1" ,"$selected_tasks.selected_algorithms.input_options.infile2")
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46 options = params["selected_tasks"]["selected_algorithms"]["options"]
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47 selected_algorithm = params["selected_tasks"]["selected_algorithms"]["selected_algorithm"]
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49 if not(selected_algorithm=="LinearSVC"):
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50 if options["kernel"]:
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51 options["kernel"] = str(options["kernel"])
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53 my_class = getattr(sklearn.svm, selected_algorithm)
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54 classifier_object = my_class(**options)
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55 classifier_object.fit(X, y)
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57 pickle.dump(classifier_object,open("$outfile_fit", 'w+'))
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59 #end if
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61 ]]>
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62 </configfile>
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63 </configfiles>
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64 <inputs>
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65 <expand macro="sl_Conditional" model="zip">
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66 <param name="selected_algorithm" type="select" label="Classifier type">
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67 <option value="SVC">C-Support Vector Classification</option>
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68 <option value="NuSVC">Nu-Support Vector Classification</option>
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69 <option value="LinearSVC">Linear Support Vector Classification</option>
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70 </param>
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71 <when value="SVC">
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72 <expand macro="sl_mixed_input"/>
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73 <expand macro="svc_advanced_options">
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74 <expand macro="C"/>
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75 </expand>
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76 </when>
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77 <when value="NuSVC">
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78 <expand macro="sl_mixed_input"/>
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79 <expand macro="svc_advanced_options">
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80 <param argument="nu" type="float" optional="true" value="0.5" label="Nu control parameter" help="Controls the number of support vectors. Should be in the interval (0, 1]. "/>
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81 </expand>
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82 </when>
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83 <when value="LinearSVC">
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84 <expand macro="sl_mixed_input"/>
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85 <section name="options" title="Advanced Options" expanded="False">
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86 <expand macro="C"/>
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87 <expand macro="tol" default_value="0.001" help_text="Tolerance for stopping criterion. "/>
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88 <expand macro="random_state" help_text="Integer number. The seed of the pseudo random number generator to use when shuffling the data for probability estimation. A fixed seed allows reproducible results."/>
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89 <!--expand macro="class_weight"/-->
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90 <param argument="max_iter" type="integer" optional="true" value="1000" label="Maximum number of iterations" help="The maximum number of iterations to be run."/>
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91 <param argument="loss" type="select" label="Loss function" help="Specifies the loss function. ''squared_hinge'' is the square of the hinge loss.">
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92 <option value="squared_hinge" selected="true">Squared hinge</option>
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93 <option value="hinge">Hinge</option>
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94 </param>
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95 <param argument="penalty" type="select" label="Penalization norm" help=" ">
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96 <option value="l1" >l1</option>
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97 <option value="l2" selected="true">l2</option>
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98 </param>
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99 <param argument="dual" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="true" label="Use the shrinking heuristic" help="Select the algorithm to either solve the dual or primal optimization problem. Prefer dual=False when n_samples > n_features."/>
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100 <param argument="multi_class" type="select" label="Multi-class strategy" help="Determines the multi-class strategy if y contains more than two classes.">
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101 <option value="ovr" selected="true">ovr</option>
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102 <option value="crammer_singer" >crammer_singer</option>
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103 </param>
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104 <param argument="fit_intercept" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="true" label="Calculate the intercept for this model" help="If set to false, data is expected to be already centered."/>
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105 <param argument="intercept_scaling" type="float" optional="true" value="1" label="Add synthetic feature to the instance vector" help=" "/>
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106 </section>
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107 </when>
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108 </expand>
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109 </inputs>
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110
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111 <expand macro="output"/>
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112
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113 <tests>
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114 <test>
13
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115 <param name="infile1" value="train_set.tabular" ftype="tabular"/>
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116 <param name="infile2" value="train_set.tabular" ftype="tabular"/>
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117 <param name="header1" value="True"/>
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118 <param name="header2" value="True"/>
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119 <param name="col1" value="1,2,3,4"/>
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120 <param name="col2" value="5"/>
0
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121 <param name="selected_task" value="train"/>
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122 <param name="selected_algorithm" value="SVC"/>
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123 <param name="random_state" value="5"/>
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124 <output name="outfile_fit" file="svc_model01.txt"/>
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125 </test>
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126 <test>
13
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127 <param name="infile1" value="train_set.tabular" ftype="tabular"/>
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128 <param name="infile2" value="train_set.tabular" ftype="tabular"/>
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129 <param name="header1" value="True"/>
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130 <param name="header2" value="True"/>
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131 <param name="col1" value="1,2,3,4"/>
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132 <param name="col2" value="5"/>
0
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133 <param name="selected_task" value="train"/>
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134 <param name="selected_algorithm" value="NuSVC"/>
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135 <param name="random_state" value="5"/>
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136 <output name="outfile_fit" file="svc_model02.txt"/>
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137 </test>
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138 <test>
13
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139 <param name="infile1" value="train_set.tabular" ftype="tabular"/>
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140 <param name="infile2" value="train_set.tabular" ftype="tabular"/>
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141 <param name="header1" value="True"/>
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142 <param name="header2" value="True"/>
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143 <param name="col1" value="1,2,3,4"/>
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144 <param name="col2" value="5"/>
0
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145 <param name="selected_task" value="train"/>
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146 <param name="selected_algorithm" value="LinearSVC"/>
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147 <param name="random_state" value="5"/>
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148 <output name="outfile_fit" file="svc_model03.txt"/>
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149 </test>
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150 <test>
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151 <param name="infile_model" value="svc_model01.txt" ftype="txt"/>
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152 <param name="infile_data" value="test_set.tabular" ftype="tabular"/>
13
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153 <param name="header" value="True"/>
0
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154 <param name="selected_task" value="load"/>
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155 <output name="outfile_predict" file="svc_prediction_result01.tabular"/>
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156 </test>
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157 <test>
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158 <param name="infile_model" value="svc_model02.txt" ftype="txt"/>
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159 <param name="infile_data" value="test_set.tabular" ftype="tabular"/>
13
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160 <param name="header" value="True"/>
0
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161 <param name="selected_task" value="load"/>
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162 <output name="outfile_predict" file="svc_prediction_result02.tabular"/>
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163 </test>
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164 <test>
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165 <param name="infile_model" value="svc_model03.txt" ftype="txt"/>
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166 <param name="infile_data" value="test_set.tabular" ftype="tabular"/>
13
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167 <param name="header" value="True"/>
0
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168 <param name="selected_task" value="load"/>
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169 <output name="outfile_predict" file="svc_prediction_result03.tabular"/>
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170 </test>
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171 </tests>
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172 <help><![CDATA[
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173 **What it does**
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174 This module implements the Support Vector Machine (SVM) classification algorithms.
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175 Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection.
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176
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177 **The advantages of support vector machines are:**
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178
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179 1- Effective in high dimensional spaces.
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180
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181 2- Still effective in cases where number of dimensions is greater than the number of samples.
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182
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183 3- Uses a subset of training points in the decision function (called support vectors), so it is also memory efficient.
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184
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185 4- Versatile: different Kernel functions can be specified for the decision function. Common kernels are provided, but it is also possible to specify custom kernels.
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186
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187 **The disadvantages of support vector machines include:**
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188
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189 1- If the number of features is much greater than the number of samples, the method is likely to give poor performances.
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190
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191 2- SVMs do not directly provide probability estimates, these are calculated using an expensive five-fold cross-validation
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192
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193 For more information check http://scikit-learn.org/stable/modules/neighbors.html
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194
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195 ]]>
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196 </help>
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197 <expand macro="sklearn_citation"/>
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198 </tool>