update

doc/LectureNotes/DataFiles/cancer.dot

@@ -6,23 +6,23 @@ edge [fontname="helvetica"] ;
 0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
 2 [label="worst concave points <= 0.135\ngini = 0.031\nsamples = 253\nvalue = [[249, 4]\n[4, 249]]", fillcolor="#e78946"] ;
 1 -> 2 ;
-3 [label="radius error <= 0.643\ngini = 0.008\nsamples = 242\nvalue = [[241, 1]\n[1, 241]]", fillcolor="#e5833c"] ;
+3 [label="area error <= 48.975\ngini = 0.008\nsamples = 242\nvalue = [[241, 1]\n[1, 241]]", fillcolor="#e5833c"] ;
 2 -> 3 ;
 4 [label="gini = 0.0\nsamples = 239\nvalue = [[239, 0]\n[0, 239]]", fillcolor="#e58139"] ;
 3 -> 4 ;
-5 [label="worst perimeter <= 87.07\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#fdf6f0"] ;
+5 [label="mean perimeter <= 78.51\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#fdf6f0"] ;
 3 -> 5 ;
 6 [label="gini = 0.0\nsamples = 1\nvalue = [[0, 1]\n[1, 0]]", fillcolor="#e58139"] ;
 5 -> 6 ;
 7 [label="gini = 0.0\nsamples = 2\nvalue = [[2, 0]\n[0, 2]]", fillcolor="#e58139"] ;
 5 -> 7 ;
-8 [label="worst texture <= 29.455\ngini = 0.397\nsamples = 11\nvalue = [[8, 3]\n[3, 8]]", fillcolor="#fae9dd"] ;
+8 [label="mean texture <= 20.84\ngini = 0.397\nsamples = 11\nvalue = [[8, 3]\n[3, 8]]", fillcolor="#fae9dd"] ;
 2 -> 8 ;
 9 [label="gini = 0.0\nsamples = 8\nvalue = [[8, 0]\n[0, 8]]", fillcolor="#e58139"] ;
 8 -> 9 ;
 10 [label="gini = 0.0\nsamples = 3\nvalue = [[0, 3]\n[3, 0]]", fillcolor="#e58139"] ;
 8 -> 10 ;
-11 [label="mean texture <= 16.22\ngini = 0.278\nsamples = 6\nvalue = [[1, 5]\n[5, 1]]", fillcolor="#f4caac"] ;
+11 [label="area error <= 13.475\ngini = 0.278\nsamples = 6\nvalue = [[1, 5]\n[5, 1]]", fillcolor="#f4caac"] ;
 1 -> 11 ;
 12 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ;
 11 -> 12 ;
@@ -30,11 +30,11 @@ edge [fontname="helvetica"] ;
 11 -> 13 ;
 14 [label="worst texture <= 20.645\ngini = 0.202\nsamples = 167\nvalue = [[19, 148]\n[148, 19]]", fillcolor="#f0b68c"] ;
 0 -> 14 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
-15 [label="worst concavity <= 0.318\ngini = 0.375\nsamples = 16\nvalue = [[12, 4]\n[4, 12]]", fillcolor="#f9e3d4"] ;
+15 [label="worst perimeter <= 116.8\ngini = 0.375\nsamples = 16\nvalue = [[12, 4]\n[4, 12]]", fillcolor="#f9e3d4"] ;
 14 -> 15 ;
 16 [label="gini = 0.0\nsamples = 11\nvalue = [[11, 0]\n[0, 11]]", fillcolor="#e58139"] ;
 15 -> 16 ;
-17 [label="mean radius <= 15.06\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#f6d5bd"] ;
+17 [label="smoothness error <= 0.005\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#f6d5bd"] ;
 15 -> 17 ;
 18 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ;
 17 -> 18 ;
@@ -48,7 +48,7 @@ edge [fontname="helvetica"] ;
 21 -> 22 ;
 23 [label="gini = 0.0\nsamples = 6\nvalue = [[6, 0]\n[0, 6]]", fillcolor="#e58139"] ;
 21 -> 23 ;
-24 [label="worst smoothness <= 0.096\ngini = 0.015\nsamples = 136\nvalue = [[1, 135]\n[135, 1]]", fillcolor="#e6853f"] ;
+24 [label="mean smoothness <= 0.079\ngini = 0.015\nsamples = 136\nvalue = [[1, 135]\n[135, 1]]", fillcolor="#e6853f"] ;
 20 -> 24 ;
 25 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ;
 24 -> 25 ;

doc/LectureNotes/_build/html/week46.html

@@ -1119,7 +1119,8 @@ doconce format html week46.do.txt --no_mako -->
 <li><p>Readings and Videos:</p>
 <ul>
 <li><p>These lecture notes</p></li>
-<li><p><a class="reference external" href="https://youtu.be/">Video of lecture to be added</a></p></li>
+<li><p><a class="reference external" href="https://youtu.be/PMswUwhYa7k">Video of lecture</a></p></li>
+<li><p><a class="reference external" href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesNov16.pdf">Whiteboard notes</a></p></li>
 <li><p><a class="reference external" href="https://www.youtube.com/watch?v=RmajweUFKvM&amp;ab_channel=Simplilearn">Video on Decision trees</a></p></li>
 <li><p>Decision Trees: Geron’s chapter 6 covers decision trees while ensemble models, voting and bagging are discussed in chapter 7. See also lecture from <a class="reference external" href="https://www.uio.no/studier/emner/matnat/math/STK-IN4300/h20/slides/lecture_7.pdf">STK-IN4300, lecture 7</a>. Chapter 9.2 of Hastie et al contains also a good discussion.</p></li>
 </ul>
@@ -1288,9 +1289,9 @@ predicting the target features of query instances is as follows:</p>
 </div>
 <div class="cell_output docutils container">
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2nd degree coefficients:
-zero power:  3.7228270501360416
-first power:  0.125304962667421
-second power:  -0.0008187895061620525
+zero power:  -7.926488923435684
+first power:  0.10587157250737213
+second power:  -0.00018629164426077816
 </pre></div>
 </div>
 <img alt="_images/week46_9_1.png" src="_images/week46_9_1.png" />

doc/LectureNotes/_build/jupyter_execute/week46.py

@@ -28,7 +28,9 @@
 # 
 #     * These lecture notes
 # 
-#     * [Video of lecture to be added](https://youtu.be/)
+#     * [Video of lecture](https://youtu.be/PMswUwhYa7k)
+# 
+#     * [Whiteboard notes](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/NotesNov16.pdf)    
 # 
 #     * [Video on Decision trees](https://www.youtube.com/watch?v=RmajweUFKvM&ab_channel=Simplilearn)
 #