update discussion

Author: RobJavVar

Diff for: book/_build/.doctrees/discussions/statistical-learning-theory.doctree

@@ -1,5 +1,6 @@
 # Discussion questions
 
-1. What fundamentally determines what is reducible error and irreducible error in a model?
+1.	Verbally describe why increasing the dimensionality of your model (by adding more variables) can increase the variance/flexibility of the model. What should factor into your choice of increasing or decreasing model complexity (i.e., increasing or decreasing the number of dimensions/variables/parameters)?
+
+2.	Is finding the “optimal” point between where minimizing error and maximizing generalizability always the goal (i.e., finding the inflection point where delta-MSE between training and test are minimized)? Are there cases where you want a more generalizable but noisier model or, conversely, a model that is overfit to a training data? Give some examples
 
-2. Verbally describe why increasing the dimensionality of your model (by adding more variables) can increase the variance/flexibility of the model.

Diff for: book/_build/.doctrees/environment.pickle

@@ -544,8 +544,8 @@ <h1>Discussion questions</h1>
   <section class="tex2jax_ignore mathjax_ignore" id="discussion-questions">
 <h1>Discussion questions<a class="headerlink" href="#discussion-questions" title="Link to this heading">#</a></h1>
 <ol class="arabic simple">
-<li><p>What fundamentally determines what is reducible error and irreducible error in a model?</p></li>
-<li><p>Verbally describe why increasing the dimensionality of your model (by adding more variables) can increase the variance/flexibility of the model.</p></li>
+<li><p>Verbally describe why increasing the dimensionality of your model (by adding more variables) can increase the variance/flexibility of the model. What should factor into your choice of increasing or decreasing model complexity (i.e., increasing or decreasing the number of dimensions/variables/parameters)?</p></li>
+<li><p>Is finding the “optimal” point between where minimizing error and maximizing generalizability always the goal (i.e., finding the inflection point where delta-MSE between training and test are minimized)? Are there cases where you want a more generalizable but noisier model or, conversely, a model that is overfit to a training data? Give some examples</p></li>
 </ol>
 </section>
 

Diff for: book/_build/html/_sources/discussions/statistical-learning-theory.md

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