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1 <?xml version="1.0" ?>
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2 <tool id="qiime_longitudinal_feature-volatility" name="qiime longitudinal feature-volatility" version="2019.4">
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3 <description> - Feature volatility analysis</description>
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4 <requirements>
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5 <requirement type="package" version="2019.4">qiime2</requirement>
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6 </requirements>
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7 <command><![CDATA[
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8 qiime longitudinal feature-volatility
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9
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10 --i-table=$itable
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4
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11
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12
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0
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13 --p-state-column="$pstatecolumn"
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14
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15 #if str($pindividualidcolumn):
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16 --p-individual-id-column="$pindividualidcolumn"
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17 #end if
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18
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19 #if $pcv:
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20 --p-cv=$pcv
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21 #end if
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22
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23 #if str($prandomstate):
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24 --p-random-state="$prandomstate"
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25 #end if
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26
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27 #set $pnjobs = '${GALAXY_SLOTS:-4}'
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28
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29 #if str($pnjobs):
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30 --p-n-jobs="$pnjobs"
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31 #end if
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32
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33
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34 #if $pnestimators:
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35 --p-n-estimators=$pnestimators
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36 #end if
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37
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38 #if str($pestimator) != 'None':
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39 --p-estimator=$pestimator
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40 #end if
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41
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42 #if $pparametertuning:
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43 --p-parameter-tuning
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44 #end if
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45
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46 #if str($pmissingsamples) != 'None':
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47 --p-missing-samples=$pmissingsamples
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48 #end if
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49
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50
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51 #if $input_files_mmetadatafile:
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52 #def list_dict_to_string(list_dict):
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53 #set $file_list = list_dict[0]['additional_input'].__getattr__('file_name')
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54 #for d in list_dict[1:]:
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55 #set $file_list = $file_list + ' --m-metadata-file=' + d['additional_input'].__getattr__('file_name')
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56 #end for
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57 #return $file_list
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58 #end def
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59 --m-metadata-file=$list_dict_to_string($input_files_mmetadatafile)
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60 #end if
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61
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62
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63 --o-filtered-table=ofilteredtable
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64 --o-feature-importance=ofeatureimportance
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65 --o-volatility-plot=ovolatilityplot
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66 --o-accuracy-results=oaccuracyresults
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67 --o-sample-estimator=osampleestimator
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68 ;
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69 cp ofilteredtable.qza $ofilteredtable;
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70 cp ofeatureimportance.qza $ofeatureimportance;
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71 qiime tools export --input-path ovolatilityplot.qzv --output-path out && mkdir -p '$ovolatilityplot.files_path'
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72 && cp -r out/* '$ovolatilityplot.files_path'
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73 && mv '$ovolatilityplot.files_path/index.html' '$ovolatilityplot';
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74 qiime tools export --input-path oaccuracyresults.qzv --output-path out && mkdir -p '$oaccuracyresults.files_path'
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75 && cp -r out/* '$oaccuracyresults.files_path'
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76 && mv '$oaccuracyresults.files_path/index.html' '$oaccuracyresults';
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77 cp osampleestimator.qza $osampleestimator
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78 ]]></command>
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79 <inputs>
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80 <param format="qza,no_unzip.zip" label="--i-table: ARTIFACT FeatureTable[Frequency] Feature table containing all features that should be used for target prediction. [required]" name="itable" optional="False" type="data"/>
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81 <param label="--p-state-column: TEXT Metadata containing collection time (state) values for each sample. Must contain exclusively numeric values. [required]" name="pstatecolumn" optional="False" type="text"/>
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82 <param label="--p-individual-id-column: TEXT Metadata column containing IDs for individual subjects. [optional]" name="pindividualidcolumn" optional="True" type="text"/>
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83 <param label="--p-cv: INTEGER Number of k-fold cross-validations to perform. Range(1, None) [default: 5]" name="pcv" optional="True" type="integer" min="1" value="5"/>
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84 <param label="--p-random-state: INTEGER Seed used by random number generator. [optional]" name="prandomstate" optional="True" type="integer"/>
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85 <param label="--p-n-estimators: INTEGER Range(1, None) Number of trees to grow for estimation. More trees will improve predictive accuracy up to a threshold level, but will also increase time and memory requirements. This parameter only affects ensemble estimators, such as Random Forest, AdaBoost, ExtraTrees, and GradientBoosting. [default: 100]" name="pnestimators" optional="True" type="integer" min="1" value="100"/>
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86 <param label="--p-estimator: " name="pestimator" optional="True" type="select">
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87 <option selected="True" value="None">Selection is Optional</option>
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88 <option value="RandomForestRegressor">RandomForestRegressor</option>
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89 <option value="ExtraTreesRegressor">ExtraTreesRegressor</option>
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90 <option value="GradientBoostingRegressor">GradientBoostingRegressor</option>
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91 <option value="AdaBoostRegressor">AdaBoostRegressor</option>
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92 <option value="ElasticNet">ElasticNet</option>
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93 <option value="Ridge">Ridge</option>
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94 <option value="Lasso">Lasso</option>
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95 <option value="KNeighborsRegressor">KNeighborsRegressor</option>
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96 <option value="LinearSVR">LinearSVR</option>
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97 <option value="SVR">SVR</option>
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98 </param>
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99 <param label="--p-parameter-tuning: --p-no-parameter-tuning Automatically tune hyperparameters using random grid search. [default: False]" name="pparametertuning" selected="False" type="boolean"/>
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100 <param label="--p-missing-samples: " name="pmissingsamples" optional="True" type="select">
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101 <option selected="True" value="None">Selection is Optional</option>
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102 <option value="error">error</option>
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103 <option value="ignore">ignore</option>
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104 </param>
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105
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4
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106 <repeat name="input_files_mmetadatafile" optional="True" title="--m-metadata-file [required]">
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0
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107 <param label="--m-metadata-file: Metadata file or artifact viewable as metadata. This option may be supplied multiple times to merge metadata. [optional]" name="additional_input" type="data" format="tabular,qza,no_unzip.zip" />
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108 </repeat>
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109
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110 </inputs>
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111 <outputs>
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112 <data format="qza" label="${tool.name} on ${on_string}: filteredtable.qza" name="ofilteredtable"/>
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113 <data format="qza" label="${tool.name} on ${on_string}: featureimportance.qza" name="ofeatureimportance"/>
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114 <data format="html" label="${tool.name} on ${on_string}: volatilityplot.qzv" name="ovolatilityplot"/>
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115 <data format="html" label="${tool.name} on ${on_string}: accuracyresults.qzv" name="oaccuracyresults"/>
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116 <data format="qza" label="${tool.name} on ${on_string}: sampleestimator.qza" name="osampleestimator"/>
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117 </outputs>
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118 <help><![CDATA[
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119 Feature volatility analysis
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120 ###########################
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121
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122 Identify features that are predictive of a numeric metadata column,
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123 state_column (e.g., time), and plot their relative frequencies across
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124 states using interactive feature volatility plots. A supervised learning
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125 regressor is used to identify important features and assess their ability
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126 to predict sample states. state_column will typically be a measure of time,
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127 but any numeric metadata column can be used.
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128
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129 Parameters
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130 ----------
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131 table : FeatureTable[Frequency]
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132 Feature table containing all features that should be used for target
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133 prediction.
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134 metadata : Metadata
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135 Sample metadata file containing individual_id_column.
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136 state_column : Str
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137 Metadata containing collection time (state) values for each sample.
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138 Must contain exclusively numeric values.
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139 individual_id_column : Str, optional
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140 Metadata column containing IDs for individual subjects.
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141 cv : Int % Range(1, None), optional
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142 Number of k-fold cross-validations to perform.
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143 random_state : Int, optional
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144 Seed used by random number generator.
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145 n_estimators : Int % Range(1, None), optional
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146 Number of trees to grow for estimation. More trees will improve
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147 predictive accuracy up to a threshold level, but will also increase
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148 time and memory requirements. This parameter only affects ensemble
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149 estimators, such as Random Forest, AdaBoost, ExtraTrees, and
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150 GradientBoosting.
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151 estimator : Str % Choices('RandomForestRegressor', 'ExtraTreesRegressor', 'GradientBoostingRegressor', 'AdaBoostRegressor', 'ElasticNet', 'Ridge', 'Lasso', 'KNeighborsRegressor', 'LinearSVR', 'SVR'), optional
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152 Estimator method to use for sample prediction.
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153 parameter_tuning : Bool, optional
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154 Automatically tune hyperparameters using random grid search.
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155 missing_samples : Str % Choices('error', 'ignore'), optional
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156 How to handle missing samples in metadata. "error" will fail if missing
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157 samples are detected. "ignore" will cause the feature table and
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158 metadata to be filtered, so that only samples found in both files are
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159 retained.
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160
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161 Returns
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162 -------
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163 filtered_table : FeatureTable[RelativeFrequency]
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164 Feature table containing only important features.
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165 feature_importance : FeatureData[Importance]
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166 Importance of each input feature to model accuracy.
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167 volatility_plot : Visualization
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168 Interactive volatility plot visualization.
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169 accuracy_results : Visualization
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170 Accuracy results visualization.
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171 sample_estimator : SampleEstimator[Regressor]
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172 Trained sample regressor.
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173 ]]></help>
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174 <macros>
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175 <import>qiime_citation.xml</import>
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176 </macros>
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177 <expand macro="qiime_citation"/>
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178 </tool>
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