update

2024-11-17 14:52:45 +01:00
parent 04ddef28e3
commit df2f85227b
16 changed files with 220 additions and 227 deletions
@@ -367,7 +367,6 @@ MathJax.Hub.Config({
 <li> Video on Gradient boost, part 1, parts 2-4 follow thereafter <a href="https://www.youtube.com/watch?v=3CC4N4z3GJc" target="_self"><tt>https://www.youtube.com/watch?v=3CC4N4z3GJc</tt></a></li>
 <li> Decision Trees: Rashcka et al chapter 3 pages 86-98, and chapter 7 on Ensemble methods, Voting and Bagging and Gradient Boosting. See also lecture from STK-IN4300, lecture 7 at <a href="https://www.uio.no/studier/emner/matnat/math/STK-IN4300/h20/slides/lecture_7.pdf" target="_self"><tt>https://www.uio.no/studier/emner/matnat/math/STK-IN4300/h20/slides/lecture_7.pdf</tt></a>.</li> 
 </ol>
-<li> 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 href="https://www.uio.no/studier/emner/matnat/math/STK-IN4300/h20/slides/lecture_7.pdf" target="_self">STK-IN4300, lecture 7</a>. Chapter 9.2 of Hastie et al contains also a good discussion.</li>
 </ol>
 </div>
 </div>
@@ -227,7 +227,6 @@ MathJax.Hub.Config({
 <p><li> Decision Trees: Rashcka et al chapter 3 pages 86-98, and chapter 7 on Ensemble methods, Voting and Bagging and Gradient Boosting. See also lecture from STK-IN4300, lecture 7 at <a href="https://www.uio.no/studier/emner/matnat/math/STK-IN4300/h20/slides/lecture_7.pdf" target="_blank"><tt>https://www.uio.no/studier/emner/matnat/math/STK-IN4300/h20/slides/lecture_7.pdf</tt></a>.</li> 
 </ol>
 <p>
-<p><li> 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 href="https://www.uio.no/studier/emner/matnat/math/STK-IN4300/h20/slides/lecture_7.pdf" target="_blank">STK-IN4300, lecture 7</a>. Chapter 9.2 of Hastie et al contains also a good discussion.</li>
 </ol>
 </div>
 </section>
@@ -325,7 +325,6 @@ MathJax.Hub.Config({
 <li> Video on Gradient boost, part 1, parts 2-4 follow thereafter <a href="https://www.youtube.com/watch?v=3CC4N4z3GJc" target="_blank"><tt>https://www.youtube.com/watch?v=3CC4N4z3GJc</tt></a></li>
 <li> Decision Trees: Rashcka et al chapter 3 pages 86-98, and chapter 7 on Ensemble methods, Voting and Bagging and Gradient Boosting. See also lecture from STK-IN4300, lecture 7 at <a href="https://www.uio.no/studier/emner/matnat/math/STK-IN4300/h20/slides/lecture_7.pdf" target="_blank"><tt>https://www.uio.no/studier/emner/matnat/math/STK-IN4300/h20/slides/lecture_7.pdf</tt></a>.</li> 
 </ol>
-<li> 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 href="https://www.uio.no/studier/emner/matnat/math/STK-IN4300/h20/slides/lecture_7.pdf" target="_blank">STK-IN4300, lecture 7</a>. Chapter 9.2 of Hastie et al contains also a good discussion.</li>
 </ol>
 </div>

@@ -402,7 +402,6 @@ MathJax.Hub.Config({
 <li> Video on Gradient boost, part 1, parts 2-4 follow thereafter <a href="https://www.youtube.com/watch?v=3CC4N4z3GJc" target="_blank"><tt>https://www.youtube.com/watch?v=3CC4N4z3GJc</tt></a></li>
 <li> Decision Trees: Rashcka et al chapter 3 pages 86-98, and chapter 7 on Ensemble methods, Voting and Bagging and Gradient Boosting. See also lecture from STK-IN4300, lecture 7 at <a href="https://www.uio.no/studier/emner/matnat/math/STK-IN4300/h20/slides/lecture_7.pdf" target="_blank"><tt>https://www.uio.no/studier/emner/matnat/math/STK-IN4300/h20/slides/lecture_7.pdf</tt></a>.</li> 
 </ol>
-<li> 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 href="https://www.uio.no/studier/emner/matnat/math/STK-IN4300/h20/slides/lecture_7.pdf" target="_blank">STK-IN4300, lecture 7</a>. Chapter 9.2 of Hastie et al contains also a good discussion.</li>
 </ol>
 </div>

@@ -1,57 +1,57 @@
 digraph Tree {
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-edge [fontname=helvetica] ;
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-1 [label="worst concave points <= 0.159\ngini = 0.067\nsamples = 259\nvalue = [[250, 9]\n[9, 250]]", fillcolor="#e58139db"] ;
+node [shape=box, style="filled, rounded", color="black", fontname="helvetica"] ;
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 1 -> 2 ;
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 2 -> 3 ;
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 3 -> 4 ;
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 5 -> 7 ;
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+8 [label="mean texture <= 20.84\ngini = 0.397\nsamples = 11\nvalue = [[8, 3]\n[3, 8]]", fillcolor="#fae9dd"] ;
 2 -> 8 ;
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 8 -> 10 ;
-11 [label="mean texture <= 16.22\ngini = 0.278\nsamples = 6\nvalue = [[1, 5]\n[5, 1]]", fillcolor="#e581396b"] ;
+11 [label="worst texture <= 24.785\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="#e58139ff"] ;
+12 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ;
 11 -> 12 ;
-13 [label="gini = 0.0\nsamples = 5\nvalue = [[0, 5]\n[5, 0]]", fillcolor="#e58139ff"] ;
+13 [label="gini = 0.0\nsamples = 5\nvalue = [[0, 5]\n[5, 0]]", fillcolor="#e58139"] ;
 11 -> 13 ;
-14 [label="worst texture <= 20.645\ngini = 0.202\nsamples = 167\nvalue = [[19, 148]\n[148, 19]]", fillcolor="#e5813994"] ;
+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 radius <= 17.74\ngini = 0.375\nsamples = 16\nvalue = [[12, 4]\n[4, 12]]", fillcolor="#e5813938"] ;
+15 [label="worst concavity <= 0.318\ngini = 0.375\nsamples = 16\nvalue = [[12, 4]\n[4, 12]]", fillcolor="#f9e3d4"] ;
 14 -> 15 ;
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 15 -> 16 ;
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 15 -> 17 ;
-18 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139ff"] ;
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 17 -> 18 ;
-19 [label="gini = 0.0\nsamples = 4\nvalue = [[0, 4]\n[4, 0]]", fillcolor="#e58139ff"] ;
+19 [label="gini = 0.0\nsamples = 4\nvalue = [[0, 4]\n[4, 0]]", fillcolor="#e58139"] ;
 17 -> 19 ;
-20 [label="mean concave points <= 0.049\ngini = 0.088\nsamples = 151\nvalue = [[7, 144]\n[144, 7]]", fillcolor="#e58139d0"] ;
+20 [label="mean concave points <= 0.049\ngini = 0.088\nsamples = 151\nvalue = [[7, 144]\n[144, 7]]", fillcolor="#ea985d"] ;
 14 -> 20 ;
-21 [label="concave points error <= 0.01\ngini = 0.48\nsamples = 15\nvalue = [[6, 9]\n[9, 6]]", fillcolor="#e5813900"] ;
+21 [label="concave points error <= 0.01\ngini = 0.48\nsamples = 15\nvalue = [[6, 9]\n[9, 6]]", fillcolor="#ffffff"] ;
 20 -> 21 ;
-22 [label="gini = 0.0\nsamples = 9\nvalue = [[0, 9]\n[9, 0]]", fillcolor="#e58139ff"] ;
+22 [label="gini = 0.0\nsamples = 9\nvalue = [[0, 9]\n[9, 0]]", fillcolor="#e58139"] ;
 21 -> 22 ;
-23 [label="gini = 0.0\nsamples = 6\nvalue = [[6, 0]\n[0, 6]]", fillcolor="#e58139ff"] ;
+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="#e58139f7"] ;
+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="#e58139ff"] ;
+25 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ;
 24 -> 25 ;
-26 [label="gini = 0.0\nsamples = 135\nvalue = [[0, 135]\n[135, 0]]", fillcolor="#e58139ff"] ;
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 24 -> 26 ;
 }
@@ -1,13 +1,13 @@
 digraph Tree {
-node [shape=box, style="filled, rounded", color="black", fontname=helvetica] ;
-edge [fontname=helvetica] ;
-0 [label="X[7] <= 0.5\ngini = 0.48\nsamples = 15\nvalue = [4, 10, 1]", fillcolor="#39e5818b"] ;
-1 [label="X[1] <= 0.5\ngini = 0.408\nsamples = 14\nvalue = [4, 10, 0]", fillcolor="#39e58199"] ;
+node [shape=box, style="filled, rounded", color="black", fontname="helvetica"] ;
+edge [fontname="helvetica"] ;
+0 [label="x[13] <= 0.5\ngini = 0.48\nsamples = 15\nvalue = [4, 10, 1]", fillcolor="#93f1ba"] ;
+1 [label="x[1] <= 0.5\ngini = 0.408\nsamples = 14\nvalue = [4, 10, 0]", fillcolor="#88efb3"] ;
 0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
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 1 -> 2 ;
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 1 -> 3 ;
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 0 -> 4 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
 }
@@ -23,7 +23,6 @@ o Readings and Videos:
  o Video on AdaBoost URL:"https://www.youtube.com/watch?v=LsK-xG1cLYA"
  o Video on Gradient boost, part 1, parts 2-4 follow thereafter URL:"https://www.youtube.com/watch?v=3CC4N4z3GJc"
  o Decision Trees: Rashcka et al chapter 3 pages 86-98, and chapter 7 on Ensemble methods, Voting and Bagging and Gradient Boosting. See also lecture from STK-IN4300, lecture 7 at URL:"https://www.uio.no/studier/emner/matnat/math/STK-IN4300/h20/slides/lecture_7.pdf". 
-* Decision Trees: Geron's chapter 6 covers decision trees while ensemble models, voting and bagging are discussed in chapter 7. See also lecture from "STK-IN4300, lecture 7":"https://www.uio.no/studier/emner/matnat/math/STK-IN4300/h20/slides/lecture_7.pdf". Chapter 9.2 of Hastie et al contains also a good discussion.
 !eblock