updating dim red stuff

doc/src/DimRed/DimRed.do.txt

@@ -1,4 +1,4 @@
-TITLE: Data Analysis and Machine Learning: Dimensionality Reduction
+TITLE: Data Analysis and Machine Learning: Preprocessing and Dimensionality Reduction
 AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of Physics, University of Oslo & Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
 DATE: today
 
@@ -19,6 +19,23 @@ reduction techniques: the principal component analysis PCA, Kernel PCA, and Loca
 !eblock
 
 
+!split
+===== Preprocessing our data  =====
+!bblock
+
+Before we proceed however, we will discuss how to preprocess our
+data. Till now and in connection with project 1 not met so many cases
+where we are too sensitive to the scaling of our data. Normally the
+data may need a rescaling and/or may be sensitive to extreme
+values. Scaling the data renders our inputs much more suitable for the
+algorithms we want to emply.
+
+_Scikit-Learn_ has several functions which allow us to rescale the data, normally resulting in much better results in terms of various accuracy scores.  The _StandardScaler_ function in _Scikit-Learn_ ensures that for each feature/predictor we study the mean value is zero and the variance is zero (every column in the design/feature matrix).
+This scaling has the drawback that it does not ensure that we have a particular maximum or minumum in our data set. Another function included in _Scikit-Learn_ is the _MinMaxScaler_ which ensures that all features are exactly between $0$ and $1$. The _Normalizer_ function scale each column of the design matrix so that 
+
+!eblock
+
+
 
 !split
 ===== Principal Component Analysis =====