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mhjensen
2019-11-03 09:46:03 +01:00
parent 3d50bd7ecb
commit fbf17fb1c6
10 changed files with 40 additions and 12 deletions
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doc/pub/DecisionTrees/html/._DecisionTrees-bs000.html
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@@ -235,7 +235,7 @@ 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>Nov 2, 2019</h4></center> <!-- date -->
<center><h4>Nov 3, 2019</h4></center> <!-- date -->
<br>
<p>
doc/pub/DecisionTrees/html/._DecisionTrees-bs002.html
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@@ -219,7 +219,10 @@ MathJax.Hub.Config({
<h2 id="___sec1" class="anchor">A typical Decision Tree with its pertinent Jargon, Classification Problem </h2>
<p>
Figure to come here.
<br /><br /><center><p><img src="DataFiles/cancer.png" align="bottom" width=600></p></center><br /><br />
<p>
This tree was produced using the Wisconsin cancer data (discussed here as well, see code examples below) using <b>Scikit-Learn</b>'s decision tree classifier. Here we have used the so-called <b>gini</b> index (see below) to split the various branches.
<p>
<p>
doc/pub/DecisionTrees/html/DecisionTrees-bs.html
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@@ -235,7 +235,7 @@ 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>Nov 2, 2019</h4></center> <!-- date -->
<center><h4>Nov 3, 2019</h4></center> <!-- date -->
<br>
<p>
doc/pub/DecisionTrees/html/DecisionTrees-reveal.html
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@@ -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>Nov 2, 2019</h4></center> <!-- date -->
<center><h4>Nov 3, 2019</h4></center> <!-- date -->
<br>
<p>
@@ -196,7 +196,10 @@ given some assumptions, make predictions about the target feature value
<h2 id="___sec1">A typical Decision Tree with its pertinent Jargon, Classification Problem </h2>
<p>
Figure to come here.
<br /><br /><center><p><img src="DataFiles/cancer.png" align="bottom" width=600></p></center><br /><br />
<p>
This tree was produced using the Wisconsin cancer data (discussed here as well, see code examples below) using <b>Scikit-Learn</b>'s decision tree classifier. Here we have used the so-called <b>gini</b> index (see below) to split the various branches.
</section>
doc/pub/DecisionTrees/html/DecisionTrees-solarized.html
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@@ -175,7 +175,7 @@ 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>Nov 2, 2019</h4></center> <!-- date -->
<center><h4>Nov 3, 2019</h4></center> <!-- date -->
<br>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -217,7 +217,10 @@ given some assumptions, make predictions about the target feature value
<h2 id="___sec1">A typical Decision Tree with its pertinent Jargon, Classification Problem </h2>
<p>
Figure to come here.
<br /><br /><center><p><img src="DataFiles/cancer.png" align="bottom" width=600></p></center><br /><br />
<p>
This tree was produced using the Wisconsin cancer data (discussed here as well, see code examples below) using <b>Scikit-Learn</b>'s decision tree classifier. Here we have used the so-called <b>gini</b> index (see below) to split the various branches.
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -2024,6 +2027,8 @@ error_going_up = <span style="color: #B452CD">0</span>
<h2 id="___sec49">XGBoost: Extreme Gradient Boosting </h2>
<p>
<!-- ------------------- end of main content --------------- -->
doc/pub/DecisionTrees/html/DecisionTrees.html
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@@ -180,7 +180,7 @@ 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>Nov 2, 2019</h4></center> <!-- date -->
<center><h4>Nov 3, 2019</h4></center> <!-- date -->
<br>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -222,7 +222,10 @@ given some assumptions, make predictions about the target feature value
<h2 id="___sec1">A typical Decision Tree with its pertinent Jargon, Classification Problem </h2>
<p>
Figure to come here.
<br /><br /><center><p><img src="DataFiles/cancer.png" align="bottom" width=600></p></center><br /><br />
<p>
This tree was produced using the Wisconsin cancer data (discussed here as well, see code examples below) using <b>Scikit-Learn</b>'s decision tree classifier. Here we have used the so-called <b>gini</b> index (see below) to split the various branches.
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -2029,6 +2032,8 @@ error_going_up <span style="color: #666666">=</span> <span style="color: #666666
<h2 id="___sec49">XGBoost: Extreme Gradient Boosting </h2>
<p>
<!-- ------------------- end of main content --------------- -->
doc/pub/DecisionTrees/ipynb/DecisionTrees.ipynb
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@@ -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: **Nov 2, 2019**\n",
"Date: **Nov 3, 2019**\n",
"\n",
"Copyright 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
"\n",
@@ -47,8 +47,16 @@
"\n",
"## A typical Decision Tree with its pertinent Jargon, Classification Problem\n",
"\n",
"Figure to come here.\n",
"<!-- dom:FIGURE: [DataFiles/cancer.png, width=600 frac=0.8] -->\n",
"<!-- begin figure -->\n",
"\n",
"<p></p>\n",
"<img src=\"DataFiles/cancer.png\" width=600>\n",
"\n",
"<!-- end figure -->\n",
"\n",
"\n",
"This tree was produced using the Wisconsin cancer data (discussed here as well, see code examples below) using **Scikit-Learn**'s decision tree classifier. Here we have used the so-called **gini** index (see below) to split the various branches.\n",
"\n",
"\n",
"\n",
doc/pub/DecisionTrees/ipynb/ipynb-DecisionTrees-src.tar.gz
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doc/pub/DecisionTrees/pdf/DecisionTrees-minted.pdf
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doc/src/DecisionTrees/DecisionTrees.do.txt
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@@ -35,8 +35,9 @@ given some assumptions, make predictions about the target feature value
!split
===== A typical Decision Tree with its pertinent Jargon, Classification Problem =====
Figure to come here.
FIGURE: [DataFiles/cancer.png, width=600 frac=0.8]
This tree was produced using the Wisconsin cancer data (discussed here as well, see code examples below) using _Scikit-Learn_'s decision tree classifier. Here we have used the so-called _gini_ index (see below) to split the various branches.
@@ -1700,3 +1701,6 @@ print("Minimum validation MSE:", min_val_error)
!split
===== XGBoost: Extreme Gradient Boosting =====