MAINT: sample-classifier tutorial style-match (#197)

source/tutorials/sample-classifier.rst

@@ -34,11 +34,11 @@ Next, we will attempt to predict which body site each sample originated from bas
      --i-table moving-pictures-table.qza \
      --m-metadata-file moving-pictures-sample-metadata.tsv \
      --m-metadata-category BodySite \
-     --o-visualization moving-pictures-BodySite \
      --p-optimize-feature-selection \
      --p-parameter-tuning \
      --p-estimator RandomForestClassifier \
-     --p-n-estimators 100
+     --p-n-estimators 100 \
+     --o-visualization moving-pictures-BodySite.qzv
 
 The visualization produced by this command presents classification accuracy results in the form of a confusion matrix. This matrix indicates how frequently a sample is classified with to the correct class vs. all other classes. The confusion matrix is displayed at the top of the visualization in the form of a heatmap, and below that as a table containing overall accuracy (the fraction of times that test samples are assigned the correct class). 
 
@@ -87,11 +87,11 @@ Next, we will attempt to predict soil relative humidity as a function of microbi
      --i-table atacama-soils-table.qza \
      --m-metadata-file atacama-soils-sample-metadata.tsv \
      --m-metadata-category PercentRelativeHumiditySoil_100 \
-     --o-visualization atacama-soils-PercentRelativeHumiditySoil_100 \
      --p-optimize-feature-selection \
      --p-parameter-tuning \
      --p-estimator RandomForestRegressor \
-     --p-n-estimators 100
+     --p-n-estimators 100 \
+     --o-visualization atacama-soils-PercentRelativeHumiditySoil_100.qzv
 
 The visualization produced by this command presents classification accuracy results in the form of a scatter plot showing predicted vs. true values for each test sample, accompanied by a linear regression line fitted to the data with 95% confidence intervals (grey shading). The true 1:1 ratio between predicted and true values is represented by a dotted line for comparison. Below this model accuracy is quantified in a table displaying mean square error and the R value, P value, standard error of the estimated gradient, slope, and intercept of the linear regression fit. The remainder of the visualization shows optional feature selection data, as described above for ``classify-samples``.