added about huber cost function

doc/pub/How2ReadData/html/How2ReadData.html

@@ -140,7 +140,7 @@ MathJax.Hub.Config({
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Aug 14, 2019</h4></center> <!-- date -->
+<center><h4>Dec 20, 2019</h4></center> <!-- date -->
 <br>
 
 <h2 id="___sec0">Introduction </h2>
@@ -1039,6 +1039,15 @@ $$
 \epsilon_{\mathrm{relative}}= \frac{\vert \hat{y} -\hat{\tilde{y}}\vert}{\vert \hat{y}\vert}.
 $$
 
+<p>
+The squared cost function results in an arithmetic mean-unbiased
+estimator, and the absolute-value cost function results in a
+median-unbiased estimator (in the one-dimensional case, and a
+geometric median-unbiased estimator for the multi-dimensional
+case). The squared cost function has the disadvantage that it has the tendency
+to be dominated by outliers.
+
+<p>
 We can modify easily the above Python code and plot the relative error instead
 <p>
 
@@ -1144,7 +1153,7 @@ $$
 \text{MAE}(\hat{y}, \hat{\tilde{y}}) = \frac{1}{n} \sum_{i=0}^{n-1} \left| y_i - \tilde{y}_i \right|.
 $$
 
-Finally we present the 
+We present the 
 squared logarithmic (quadratic) error
 $$
 \text{MSLE}(\hat{y}, \hat{\tilde{y}}) = \frac{1}{n} \sum_{i=0}^{n - 1} (\log_e (1 + y_i) - \log_e (1 + \tilde{y}_i) )^2,
@@ -1156,6 +1165,11 @@ estimate is best to use when targets having exponential growth, such
 as population counts, average sales of a commodity over a span of
 years etc.
 
+<p>
+Finally, another cost function is the Huber cost function used in robust regression.
+It is less sensitive to outliers in data than the squared error cost function.
+A variant for classification is also sometimes used, a quantity we will meet later.
+
 <p>
 We will discuss in more
 detail these and other functions in the various lectures.  We conclude this part with another example. Instead of