Update Documentation's Workflow Section

README.md

@@ -97,7 +97,7 @@ Evaluation.plot()
 
 ![img](https://raw.githubusercontent.com/sergioburdisso/pyss3/master/docs/_static/plot_evaluations.gif)
 
-In this 3D plot, each point represents an experiment/evaluation performed using that particular combination of values (s, l, and p). Also, these points are painted proportional to how good the performance was using that configuration of the model. Researchers can interactively change the evaluation metrics to be used (accuracy, precision, recall, f1, etc.) and plots will update "on the fly". Additionally, when the cursor is moved over a data point, useful information is shown (including a "compact" representation of the confusion matrix obtained in that experiment). Finally, it is worth mentioning that, before showing the 3D plots, PySS3 creates a single and portable HTML file in your project folder containing the interactive plots. This allows researchers to store, send or upload the plots to another place using this single HTML file (or even provide a link to this file in their own papers, which would be nicer for readers, plus it would increase experimentation transparency). For example, we have uploaded two of these files for you to see: ["Movie Review (Sentiment Analysis)"](https://pyss3.readthedocs.io/en/latest/_static/ss3_model_evaluation[movie_review_3grams].html) and ["Topic Categorization"](https://pyss3.readthedocs.io/en/latest/_static/ss3_model_evaluation[topic_categorization_3grams].html), both evaluation plots were also obtained following the [tutorials](https://pyss3.readthedocs.io/en/latest/user_guide/getting-started.html#tutorials).
+In this 3D plot, each point represents an experiment/evaluation performed using that particular combination of values (s, l, and p). Also, these points are painted proportional to how good the performance was according to the selected metric; the plot will update "on the fly" when the user select a different evaluation metric (accuracy, precision, recall, f1, etc.). Additionally, when the cursor is moved over a data point, useful information is shown (including a "compact" representation of the confusion matrix obtained in that experiment). Finally, it is worth mentioning that, before showing the 3D plots, PySS3 creates a single and portable HTML file in your project folder containing the interactive plots. This allows users to store, send or upload the plots to another place using this single HTML file. For example, we have uploaded two of these files for you to see: ["Sentiment Analysis (Movie Reviews)"](https://pyss3.readthedocs.io/en/latest/_static/ss3_model_evaluation[movie_review_3grams].html) and ["Topic Categorization"](https://pyss3.readthedocs.io/en/latest/_static/ss3_model_evaluation[topic_categorization_3grams].html), both evaluation plots were also obtained following the [tutorials](https://pyss3.readthedocs.io/en/latest/user_guide/getting-started.html#tutorials).
 
 
 ## The PySS3 Workflow :computer:

docs/index.rst

@@ -104,24 +104,9 @@ Now, we could also use the ``plot`` function to analyze the results obtained in
 
 .. image:: _static/plot_evaluations.gif
 
-In this 3D plot, each point represents an experiment/evaluation performed using that
-particular combination of values (s, l, and p). Also, these points are painted
-proportional to how good the performance was using that configuration of
-the model. Researchers can interactively change the evaluation metrics
-to be used (accuracy, precision, recall, f1, etc.) and plots will update
-"on the fly". Additionally, when the cursor is moved over a data point,
-useful information is shown (including a "compact" representation of the
-confusion matrix obtained in that experiment). Finally, it is worth
-mentioning that, before showing the 3D plots, PySS3 creates a
-single and portable HTML file in your project folder containing the
-interactive plots. This allows researchers to store, send or upload the
-plots to another place using this single HTML file (or even provide a
-link to this file in their own papers, which would be nicer for readers,
-plus it would increase experimentation transparency). For example, we
-have uploaded two of these files for you to see: `"Movie Review (Sentiment Analysis)" <_static/ss3_model_evaluation[movie_review_3grams].html>`__
-and `"Topic
-Categorization" <_static/ss3_model_evaluation[topic_categorization_3grams].html>`__,
-both evaluation plots were obtained following the :ref:`tutorials`.
+
+In this 3D plot, each point represents an experiment/evaluation performed using that particular combination of values (s, l, and p). Also, these points are painted proportional to how good the performance was according to the selected metric; the plot will update "on the fly" when the user select a different evaluation metric (accuracy, precision, recall, f1, etc.). Additionally, when the cursor is moved over a data point, useful information is shown (including a "compact" representation of the confusion matrix obtained in that experiment). Finally, it is worth mentioning that, before showing the 3D plots, PySS3 creates a single and portable HTML file in your project folder containing the interactive plots. This allows users to store, send or upload the plots to another place using this single HTML file. For example, we have uploaded two of these files for you to see: `"Sentiment Analysis (Movie Reviews)" <_static/ss3_model_evaluation[movie_review_3grams].html>`__ and `"Topic Categorization" <_static/ss3_model_evaluation[topic_categorization_3grams].html>`__, both evaluation plots were obtained following the :ref:`tutorials`.
+
 
 
 Want to give PySS3 a try?

docs/user_guide/workflow.rst

@@ -10,14 +10,15 @@ PySS3 provides two main types of workflow: classic and "command line". Both work
 Classic Workflow
 ================
 
-As usual, importing the needed classes and functions from the package, the user writes a python script to train and test the classifiers. In this workflow, user can use the ``PySS3 Command Line`` tool to perform model selection (though hyperparameter optimization). 
+As usual, importing the needed classes and functions from the package, the user writes a python script to train and test the classifiers.
 
 
 .. _pyss3-workflow:
 Command-Line Workflow
 =====================
 
-The whole process is done using only the ``PySS3 Command Line`` tool. This workflow provides a faster way to perform experimentations since the user doesn't have to write any python script. Plus, this Command Line tool allows the user to actively interact  "on the fly" with the models being developed.
+When you install the package (for instance by using ``pip install pyss3``) a new command (``pyss3``) is automatically added to your environment's command line. This command allows you to access to the _PySS3 Command Line_, an interactive command-line query tool. This workflow consist of using this tool to carry out the whole machine learning pipeline (model selection, training, testing, etc.), which provides a faster way to perform experimentations since the user doesn't have to write any python script. Plus, this Command Line tool allows the user to actively interact  "on the fly" with the models being developed.
+
 
 
 Note: :ref:`tutorials` are presented in two versions, one for each workflow type, so that the reader can choose the workflow that best suit her/his needs.