added about huber cost function

doc/src/How2ReadData/How2ReadData.do.txt

@@ -814,6 +814,14 @@ the relative error (why would we prefer the MSE instead of the relative error?)
 \epsilon_{\mathrm{relative}}= \frac{\vert \hat{y} -\hat{\tilde{y}}\vert}{\vert \hat{y}\vert}.
 \]
 !et
+
+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.
+
 We can modify easily the above Python code and plot the relative error instead
 !bc pycod
 import numpy as np
@@ -915,7 +923,7 @@ The MAE is defined as follows
 \text{MAE}(\hat{y}, \hat{\tilde{y}}) = \frac{1}{n} \sum_{i=0}^{n-1} \left| y_i - \tilde{y}_i \right|.
 \]
 !et
-Finally we present the 
+We present the 
 squared logarithmic (quadratic) error
 !bt
 \[
@@ -928,6 +936,13 @@ 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. 
 
+
+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. 
+
+
+
 We will discuss in more
 detail these and other functions in the various lectures.  We conclude this part with another example. Instead of 
 a linear $x$-dependence we study now a cubic polynomial and use the polynomial regression analysis tools of scikit-learn.