changed intro to trees

doc/pub/DecisionTrees/html/._DecisionTrees-bs000.html

@@ -299,7 +299,7 @@ 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>Dec 12, 2019</h4></center> <!-- date -->
+<center><h4>Dec 26, 2019</h4></center> <!-- date -->
 <br>
 <p>
 

doc/pub/DecisionTrees/html/._DecisionTrees-bs001.html

@@ -282,6 +282,14 @@ MathJax.Hub.Config({
 
 <h2 id="___sec0" class="anchor">Decision trees, overarching aims  </h2>
 
+<p>
+We start here with the most basic algorithm, the so-called decision
+tree. With this basic algorithm we can in turn build more complex
+networks, spanning from homogeneous and heterogenous forests (bagging,
+random forests and more) to one of the most popular supervised
+algorithms nowadays, the extreme gradient boosting, or just
+XGBoost. But let us start with the simplest possible ingredient.
+
 <p>
 Decision trees are supervised learning algorithms used for both,
 classification and regression tasks.

doc/pub/DecisionTrees/html/DecisionTrees-bs.html

@@ -299,7 +299,7 @@ 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>Dec 12, 2019</h4></center> <!-- date -->
+<center><h4>Dec 26, 2019</h4></center> <!-- date -->
 <br>
 <p>
 

doc/pub/DecisionTrees/html/DecisionTrees-reveal.html

@@ -148,7 +148,7 @@ MathJax.Hub.Config({
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>&nbsp;<br>
-<center><h4>Dec 12, 2019</h4></center> <!-- date -->
+<center><h4>Dec 26, 2019</h4></center> <!-- date -->
 <br>
 <p>
 
@@ -161,6 +161,14 @@ MathJax.Hub.Config({
 <section>
 <h2 id="___sec0">Decision trees, overarching aims  </h2>
 
+<p>
+We start here with the most basic algorithm, the so-called decision
+tree. With this basic algorithm we can in turn build more complex
+networks, spanning from homogeneous and heterogenous forests (bagging,
+random forests and more) to one of the most popular supervised
+algorithms nowadays, the extreme gradient boosting, or just
+XGBoost. But let us start with the simplest possible ingredient.
+
 <p>
 Decision trees are supervised learning algorithms used for both,
 classification and regression tasks.

doc/pub/DecisionTrees/html/DecisionTrees-solarized.html

@@ -224,13 +224,21 @@ 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>Dec 12, 2019</h4></center> <!-- date -->
+<center><h4>Dec 26, 2019</h4></center> <!-- date -->
 <br>
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 
 <h2 id="___sec0">Decision trees, overarching aims  </h2>
 
+<p>
+We start here with the most basic algorithm, the so-called decision
+tree. With this basic algorithm we can in turn build more complex
+networks, spanning from homogeneous and heterogenous forests (bagging,
+random forests and more) to one of the most popular supervised
+algorithms nowadays, the extreme gradient boosting, or just
+XGBoost. But let us start with the simplest possible ingredient.
+
 <p>
 Decision trees are supervised learning algorithms used for both,
 classification and regression tasks.

doc/pub/DecisionTrees/html/DecisionTrees.html

@@ -229,13 +229,21 @@ 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>Dec 12, 2019</h4></center> <!-- date -->
+<center><h4>Dec 26, 2019</h4></center> <!-- date -->
 <br>
 <p>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 
 <h2 id="___sec0">Decision trees, overarching aims  </h2>
 
+<p>
+We start here with the most basic algorithm, the so-called decision
+tree. With this basic algorithm we can in turn build more complex
+networks, spanning from homogeneous and heterogenous forests (bagging,
+random forests and more) to one of the most popular supervised
+algorithms nowadays, the extreme gradient boosting, or just
+XGBoost. But let us start with the simplest possible ingredient.
+
 <p>
 Decision trees are supervised learning algorithms used for both,
 classification and regression tasks.

doc/pub/DecisionTrees/ipynb/DecisionTrees.ipynb

@@ -10,7 +10,7 @@
     "<!-- Author: -->  \n",
     "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
     "\n",
-    "Date: **Dec 12, 2019**\n",
+    "Date: **Dec 26, 2019**\n",
     "\n",
     "Copyright 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
     "\n",
@@ -20,6 +20,13 @@
     "## Decision trees, overarching aims\n",
     "\n",
     "\n",
+    "We start here with the most basic algorithm, the so-called decision\n",
+    "tree. With this basic algorithm we can in turn build more complex\n",
+    "networks, spanning from homogeneous and heterogenous forests (bagging,\n",
+    "random forests and more) to one of the most popular supervised\n",
+    "algorithms nowadays, the extreme gradient boosting, or just\n",
+    "XGBoost. But let us start with the simplest possible ingredient.\n",
+    "\n",
     "Decision trees are supervised learning algorithms used for both,\n",
     "classification and regression tasks.\n",
     "\n",
@@ -35,6 +42,8 @@
     "informative** feature is done until we accomplish a stopping criteria\n",
     "where we then finally end up in so called **leaf nodes**. \n",
     "\n",
+    "\n",
+    "\n",
     "A decision tree is typically divided into a **root node**, the **interior nodes**,\n",
     "and the final **leaf nodes** or just **leaves**. These entities are then connected by so-called **branches**.\n",
     "\n",

doc/src/DecisionTrees/DecisionTrees.do.txt

@@ -7,6 +7,13 @@ DATE: today
 ===== Decision trees, overarching aims  =====
 
 
+We start here with the most basic algorithm, the so-called decision
+tree. With this basic algorithm we can in turn build more complex
+networks, spanning from homogeneous and heterogenous forests (bagging,
+random forests and more) to one of the most popular supervised
+algorithms nowadays, the extreme gradient boosting, or just
+XGBoost. But let us start with the simplest possible ingredient.
+
 Decision trees are supervised learning algorithms used for both,
 classification and regression tasks.
 
@@ -22,6 +29,8 @@ to be the most informative ones. The process of finding the _most
 informative_ feature is done until we accomplish a stopping criteria
 where we then finally end up in so called _leaf nodes_. 
 
+
+
 A decision tree is typically divided into a _root node_, the _interior nodes_,
 and the final _leaf nodes_ or just _leaves_. These entities are then connected by so-called _branches_.