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Morten Hjorth-Jensen
2023-11-16 07:11:29 +01:00
parent 64bbb5b739
commit 982e47382d
50 changed files with 2961 additions and 2404 deletions
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doc/LectureNotes/DataFiles/cancer.dot
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doc/LectureNotes/DataFiles/grade.dot
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doc/LectureNotes/DataFiles/grades.csv
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Grade Trend,Hours slept,Hours Studied,Grade
1,0,1,1
0,1,0,0
1,0,1,1
1,1,1,1
0,0,1,0
1,0,0,0
0,1,1,0
0,0,1,0
1,0,0,0
1,1,1,1
1 Grade Trend Hours slept Hours Studied Grade
2 1 0 1 1
3 0 1 0 0
4 1 0 1 1
5 1 1 1 1
6 0 0 1 0
7 1 0 0 0
8 0 1 1 0
9 0 0 1 0
10 1 0 0 0
11 1 1 1 1
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doc/LectureNotes/DataFiles/ride.dot
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doc/LectureNotes/_build/html/_sources/exercisesweek43.ipynb
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doc/LectureNotes/_build/html/_sources/week42.ipynb
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@@ -2635,7 +2635,7 @@
"name": "python",
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doc/LectureNotes/_build/html/_sources/week46.ipynb
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doc/LectureNotes/_build/html/exercisesweek43.html
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@@ -353,6 +353,11 @@ const thebe_selector_output = ".output, .cell_output"
Week 45, Recurrent Neural Networks
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="week46.html">
Week 46: Decision Trees, Ensemble methods and Random Forests
</a>
</li>
</ul>
<p aria-level="2" class="caption" role="heading">
<span class="caption-text">
@@ -367,7 +372,12 @@ const thebe_selector_output = ".output, .cell_output"
</li>
<li class="toctree-l1">
<a class="reference internal" href="project2.html">
Project 2 on Machine Learning, deadline November 13 (Midnight)
Project 2 on Machine Learning, deadline November 17 (Midnight)
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="project3.html">
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
</a>
</li>
</ul>
@@ -931,9 +941,8 @@ Accuracy score on data set: 0.5
Learning rate = 1e-05
Lambda = 1.0
Accuracy score on data set: 0.5
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1e-05
Learning rate = 1e-05
Lambda = 10.0
Accuracy score on data set: 0.5
@@ -1092,9 +1101,8 @@ Accuracy score on data set: 0.5
Learning rate = 10.0
Lambda = 0.01
Accuracy score on data set: 0.5
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 10.0
Learning rate = 10.0
Lambda = 0.1
Accuracy score on data set: 0.5
@@ -1129,7 +1137,7 @@ Accuracy score on data set: 0.5
warnings.warn(
</pre></div>
</div>
<img alt="_images/exercisesweek43_26_4.png" src="_images/exercisesweek43_26_4.png" />
<img alt="_images/exercisesweek43_26_2.png" src="_images/exercisesweek43_26_2.png" />
</div>
</div>
</div>
@@ -5436,6 +5444,9 @@ case.</p>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Adam: Eta=0.001, Lambda=0
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392
</pre></div>
</div>
@@ -8472,6 +8483,9 @@ case.</p>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Adam: Eta=0.0001, Lambda=0
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> [----------------------------------------] 0.000% | train_error: 11.3 | train_acc: 0.453 | val_error: 10.4 | val_acc: 0.497
</pre></div>
</div>
doc/LectureNotes/_build/html/searchindex.js
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doc/LectureNotes/_build/html/week42.html
+73 -65
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@@ -353,6 +353,11 @@ const thebe_selector_output = ".output, .cell_output"
Week 45, Recurrent Neural Networks
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="week46.html">
Week 46: Decision Trees, Ensemble methods and Random Forests
</a>
</li>
</ul>
<p aria-level="2" class="caption" role="heading">
<span class="caption-text">
@@ -367,7 +372,12 @@ const thebe_selector_output = ".output, .cell_output"
</li>
<li class="toctree-l1">
<a class="reference internal" href="project2.html">
Project 2 on Machine Learning, deadline November 13 (Midnight)
Project 2 on Machine Learning, deadline November 17 (Midnight)
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="project3.html">
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
</a>
</li>
</ul>
@@ -1697,7 +1707,7 @@ the <em>Hadamard product</em>, meaning element-wise multiplication.</p>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Old accuracy on training data: 0.1440501043841336
</pre></div>
</div>
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp
return 1/(1 + np.exp(-x))
</pre></div>
</div>
@@ -2033,7 +2043,7 @@ Lambda = 10.0
Accuracy score on test set: 0.19166666666666668
</pre></div>
</div>
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp
return 1/(1 + np.exp(-x))
</pre></div>
</div>
@@ -2042,7 +2052,7 @@ Lambda = 1e-05
Accuracy score on test set: 0.10555555555555556
</pre></div>
</div>
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp
return 1/(1 + np.exp(-x))
</pre></div>
</div>
@@ -2051,7 +2061,7 @@ Lambda = 0.0001
Accuracy score on test set: 0.08611111111111111
</pre></div>
</div>
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp
return 1/(1 + np.exp(-x))
</pre></div>
</div>
@@ -2060,7 +2070,7 @@ Lambda = 0.001
Accuracy score on test set: 0.10555555555555556
</pre></div>
</div>
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp
return 1/(1 + np.exp(-x))
</pre></div>
</div>
@@ -2069,7 +2079,7 @@ Lambda = 0.01
Accuracy score on test set: 0.08888888888888889
</pre></div>
</div>
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp
return 1/(1 + np.exp(-x))
</pre></div>
</div>
@@ -2078,7 +2088,7 @@ Lambda = 0.1
Accuracy score on test set: 0.08611111111111111
</pre></div>
</div>
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp
return 1/(1 + np.exp(-x))
</pre></div>
</div>
@@ -2087,7 +2097,7 @@ Lambda = 1.0
Accuracy score on test set: 0.08888888888888889
</pre></div>
</div>
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp
return 1/(1 + np.exp(-x))
</pre></div>
</div>
@@ -2096,11 +2106,11 @@ Lambda = 10.0
Accuracy score on test set: 0.09166666666666666
</pre></div>
</div>
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp
return 1/(1 + np.exp(-x))
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:43: RuntimeWarning: overflow encountered in exp
exp_term = np.exp(self.z_o)
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
</pre></div>
</div>
@@ -2109,11 +2119,11 @@ Lambda = 1e-05
Accuracy score on test set: 0.07777777777777778
</pre></div>
</div>
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp
return 1/(1 + np.exp(-x))
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:43: RuntimeWarning: overflow encountered in exp
exp_term = np.exp(self.z_o)
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
</pre></div>
</div>
@@ -2122,11 +2132,11 @@ Lambda = 0.0001
Accuracy score on test set: 0.07777777777777778
</pre></div>
</div>
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp
return 1/(1 + np.exp(-x))
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:43: RuntimeWarning: overflow encountered in exp
exp_term = np.exp(self.z_o)
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
</pre></div>
</div>
@@ -2135,11 +2145,11 @@ Lambda = 0.001
Accuracy score on test set: 0.07777777777777778
</pre></div>
</div>
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp
return 1/(1 + np.exp(-x))
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:43: RuntimeWarning: overflow encountered in exp
exp_term = np.exp(self.z_o)
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
</pre></div>
</div>
@@ -2148,11 +2158,11 @@ Lambda = 0.01
Accuracy score on test set: 0.07777777777777778
</pre></div>
</div>
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp
return 1/(1 + np.exp(-x))
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:43: RuntimeWarning: overflow encountered in exp
exp_term = np.exp(self.z_o)
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
</pre></div>
</div>
@@ -2161,7 +2171,7 @@ Lambda = 0.1
Accuracy score on test set: 0.07777777777777778
</pre></div>
</div>
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp
return 1/(1 + np.exp(-x))
</pre></div>
</div>
@@ -2170,11 +2180,11 @@ Lambda = 1.0
Accuracy score on test set: 0.10555555555555556
</pre></div>
</div>
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp
return 1/(1 + np.exp(-x))
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:43: RuntimeWarning: overflow encountered in exp
exp_term = np.exp(self.z_o)
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
</pre></div>
</div>
@@ -2183,11 +2193,11 @@ Lambda = 10.0
Accuracy score on test set: 0.07777777777777778
</pre></div>
</div>
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp
return 1/(1 + np.exp(-x))
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:43: RuntimeWarning: overflow encountered in exp
exp_term = np.exp(self.z_o)
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
</pre></div>
</div>
@@ -2196,11 +2206,11 @@ Lambda = 1e-05
Accuracy score on test set: 0.07777777777777778
</pre></div>
</div>
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp
return 1/(1 + np.exp(-x))
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:43: RuntimeWarning: overflow encountered in exp
exp_term = np.exp(self.z_o)
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
</pre></div>
</div>
@@ -2209,11 +2219,11 @@ Lambda = 0.0001
Accuracy score on test set: 0.07777777777777778
</pre></div>
</div>
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp
return 1/(1 + np.exp(-x))
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:43: RuntimeWarning: overflow encountered in exp
exp_term = np.exp(self.z_o)
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
</pre></div>
</div>
@@ -2222,11 +2232,11 @@ Lambda = 0.001
Accuracy score on test set: 0.07777777777777778
</pre></div>
</div>
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp
return 1/(1 + np.exp(-x))
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:43: RuntimeWarning: overflow encountered in exp
exp_term = np.exp(self.z_o)
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
</pre></div>
</div>
@@ -2235,11 +2245,11 @@ Lambda = 0.01
Accuracy score on test set: 0.07777777777777778
</pre></div>
</div>
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp
return 1/(1 + np.exp(-x))
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:43: RuntimeWarning: overflow encountered in exp
exp_term = np.exp(self.z_o)
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
</pre></div>
</div>
@@ -2248,11 +2258,11 @@ Lambda = 0.1
Accuracy score on test set: 0.07777777777777778
</pre></div>
</div>
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp
return 1/(1 + np.exp(-x))
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:43: RuntimeWarning: overflow encountered in exp
exp_term = np.exp(self.z_o)
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
</pre></div>
</div>
@@ -2261,11 +2271,11 @@ Lambda = 1.0
Accuracy score on test set: 0.07777777777777778
</pre></div>
</div>
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp
return 1/(1 + np.exp(-x))
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:43: RuntimeWarning: overflow encountered in exp
exp_term = np.exp(self.z_o)
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
</pre></div>
</div>
@@ -2318,15 +2328,15 @@ Accuracy score on test set: 0.07777777777777778
</div>
</div>
<div class="cell_output docutils container">
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp
return 1/(1 + np.exp(-x))
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp
return 1/(1 + np.exp(-x))
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp
return 1/(1 + np.exp(-x))
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp
return 1/(1 + np.exp(-x))
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp
return 1/(1 + np.exp(-x))
</pre></div>
</div>
@@ -2630,9 +2640,8 @@ Accuracy score on test set: 0.8666666666666667
Learning rate = 1.0
Lambda = 1e-05
Accuracy score on test set: 0.08611111111111111
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 1.0
Learning rate = 1.0
Lambda = 0.0001
Accuracy score on test set: 0.10555555555555556
</pre></div>
@@ -2670,13 +2679,13 @@ Accuracy score on test set: 0.11666666666666667
Learning rate = 10.0
Lambda = 0.001
Accuracy score on test set: 0.10555555555555556
Learning rate = 10.0
Lambda = 0.01
Accuracy score on test set: 0.1388888888888889
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 10.0
Lambda = 0.01
Accuracy score on test set: 0.1388888888888889
Learning rate = 10.0
Lambda = 0.1
Accuracy score on test set: 0.11388888888888889
</pre></div>
@@ -3116,9 +3125,8 @@ Accuracy score on data set: 0.5
Learning rate = 10.0
Lambda = 1e-05
Accuracy score on data set: 0.5
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Learning rate = 10.0
Learning rate = 10.0
Lambda = 0.0001
Accuracy score on data set: 0.5
@@ -3165,7 +3173,7 @@ Accuracy score on data set: 0.5
warnings.warn(
</pre></div>
</div>
<img alt="_images/week42_88_3.png" src="_images/week42_88_3.png" />
<img alt="_images/week42_88_2.png" src="_images/week42_88_2.png" />
</div>
</div>
</div>
doc/LectureNotes/_build/html/week46.html
+373 -44
View File
@@ -372,7 +372,12 @@ const thebe_selector_output = ".output, .cell_output"
</li>
<li class="toctree-l1">
<a class="reference internal" href="project2.html">
Project 2 on Machine Learning, deadline November 13 (Midnight)
Project 2 on Machine Learning, deadline November 17 (Midnight)
</a>
</li>
<li class="toctree-l1">
<a class="reference internal" href="project3.html">
Project 3 on Machine Learning, deadline December 18 (midnight), 2023
</a>
</li>
</ul>
@@ -461,16 +466,6 @@ const thebe_selector_output = ".output, .cell_output"
Basics of a tree
</a>
</li>
<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#a-sketch-of-a-tree-regression-problem">
A Sketch of a Tree, Regression problem
</a>
</li>
<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#a-sketch-of-a-tree-classification-problem">
A Sketch of a Tree, Classification problem
</a>
</li>
<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#a-typical-decision-tree-with-its-pertinent-jargon-classification-problem">
A typical Decision Tree with its pertinent Jargon, Classification Problem
@@ -801,16 +796,6 @@ const thebe_selector_output = ".output, .cell_output"
Basics of a tree
</a>
</li>
<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#a-sketch-of-a-tree-regression-problem">
A Sketch of a Tree, Regression problem
</a>
</li>
<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#a-sketch-of-a-tree-classification-problem">
A Sketch of a Tree, Classification problem
</a>
</li>
<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#a-typical-decision-tree-with-its-pertinent-jargon-classification-problem">
A typical Decision Tree with its pertinent Jargon, Classification Problem
@@ -1172,14 +1157,6 @@ learned the underlying structure of the training data and hence can,
given some assumptions, make predictions about the target feature value
(class) of unseen query instances.</p>
</div>
<div class="section" id="a-sketch-of-a-tree-regression-problem">
<h2>A Sketch of a Tree, Regression problem<a class="headerlink" href="#a-sketch-of-a-tree-regression-problem" title="Permalink to this headline"></a></h2>
<p><a class="reference external" href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2022/NotesNov32022.pdf">See handwritten notes November 3</a></p>
<!-- FIGURE: [DataFiles/Regsimpletree.png, width=600 frac=0.8] --></div>
<div class="section" id="a-sketch-of-a-tree-classification-problem">
<h2>A Sketch of a Tree, Classification problem<a class="headerlink" href="#a-sketch-of-a-tree-classification-problem" title="Permalink to this headline"></a></h2>
<p><a class="reference external" href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2022/NotesNov32022.pdf">See handwritten notes November 3</a></p>
<!-- FIGURE: [DataFiles/Classimpletree.png, width=600 frac=0.8] --></div>
<div class="section" id="a-typical-decision-tree-with-its-pertinent-jargon-classification-problem">
<h2>A typical Decision Tree with its pertinent Jargon, Classification Problem<a class="headerlink" href="#a-typical-decision-tree-with-its-pertinent-jargon-classification-problem" title="Permalink to this headline"></a></h2>
<!-- dom:FIGURE: [DataFiles/cancer.png, width=600 frac=0.8] -->
@@ -1311,13 +1288,13 @@ 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: 4.888883015934703
first power: -0.10815559091341771
second power: 0.0005603761549585715
zero power: 3.7228270501360416
first power: 0.125304962667421
second power: -0.0008187895061620525
</pre></div>
</div>
<img alt="_images/week46_11_1.png" src="_images/week46_11_1.png" />
<img alt="_images/week46_11_2.png" src="_images/week46_11_2.png" />
<img alt="_images/week46_9_1.png" src="_images/week46_9_1.png" />
<img alt="_images/week46_9_2.png" src="_images/week46_9_2.png" />
</div>
</div>
</div>
@@ -1735,7 +1712,7 @@ s = -\sum_{k=1}^K p_{mk}\log{p_{mk}}.
Text(0.9230769230769231, 0.4166666666666667, &#39;gini = 0.0\nsamples = 43\nvalue = [0, 0, 43]&#39;)]
</pre></div>
</div>
<img alt="_images/week46_43_1.png" src="_images/week46_43_1.png" />
<img alt="_images/week46_41_1.png" src="_images/week46_41_1.png" />
</div>
</div>
</div>
@@ -1950,16 +1927,117 @@ these binary classes, they can easily be split into ones and zeros.</p>
</div>
</div>
<div class="cell_output docutils container">
<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
<span class="ne">FileNotFoundError</span><span class="g g-Whitespace"> </span>Traceback (most recent call last)
<span class="nn">Input In [6],</span> in <span class="ni">&lt;cell line: 37&gt;</span><span class="nt">()</span>
<span class="g g-Whitespace"> </span><span class="mi">34</span> <span class="k">def</span> <span class="nf">save_fig</span><span class="p">(</span><span class="n">fig_id</span><span class="p">):</span>
<span class="g g-Whitespace"> </span><span class="mi">35</span> <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">image_path</span><span class="p">(</span><span class="n">fig_id</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;.png&quot;</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s1">&#39;png&#39;</span><span class="p">)</span>
<span class="ne">---&gt; </span><span class="mi">37</span> <span class="n">infile</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="n">data_path</span><span class="p">(</span><span class="s2">&quot;grades.csv&quot;</span><span class="p">),</span><span class="s1">&#39;r&#39;</span><span class="p">)</span>
<span class="g g-Whitespace"> </span><span class="mi">39</span> <span class="c1"># Read the experimental data with Pandas</span>
<span class="g g-Whitespace"> </span><span class="mi">40</span> <span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">display</span>
<div class="output text_html"><div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
<span class="ne">FileNotFoundError</span>: [Errno 2] No such file or directory: &#39;DataFiles/grades.csv&#39;
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Grade Trend</th>
<th>Hours slept</th>
<th>Hours Studied</th>
<th>Grade</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>1</td>
<td>0</td>
<td>1</td>
<td>1</td>
</tr>
<tr>
<th>1</th>
<td>0</td>
<td>1</td>
<td>0</td>
<td>0</td>
</tr>
<tr>
<th>2</th>
<td>1</td>
<td>0</td>
<td>1</td>
<td>1</td>
</tr>
<tr>
<th>3</th>
<td>1</td>
<td>1</td>
<td>1</td>
<td>1</td>
</tr>
<tr>
<th>4</th>
<td>0</td>
<td>0</td>
<td>1</td>
<td>0</td>
</tr>
<tr>
<th>5</th>
<td>1</td>
<td>0</td>
<td>0</td>
<td>0</td>
</tr>
<tr>
<th>6</th>
<td>0</td>
<td>1</td>
<td>1</td>
<td>0</td>
</tr>
<tr>
<th>7</th>
<td>0</td>
<td>0</td>
<td>1</td>
<td>0</td>
</tr>
<tr>
<th>8</th>
<td>1</td>
<td>0</td>
<td>0</td>
<td>0</td>
</tr>
<tr>
<th>9</th>
<td>1</td>
<td>1</td>
<td>1</td>
<td>1</td>
</tr>
</tbody>
</table>
</div></div><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[[1 0 1]
[0 1 0]
[1 0 1]
[1 1 1]
[0 0 1]
[1 0 0]
[0 1 1]
[0 0 1]
[1 0 0]
[1 1 1]]
Train set accuracy with Decision Tree: 1.00
</pre></div>
</div>
<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0
</pre></div>
</div>
</div>
@@ -2073,6 +2151,65 @@ humidity and weak and strong for wind.</p>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> (0, 0) 1.0
(0, 7) 1.0
(0, 9) 1.0
(0, 13) 1.0
(1, 3) 1.0
(1, 5) 1.0
(1, 8) 1.0
(1, 12) 1.0
(2, 3) 1.0
(2, 5) 1.0
(2, 8) 1.0
(2, 11) 1.0
(3, 1) 1.0
(3, 5) 1.0
(3, 8) 1.0
(3, 12) 1.0
(4, 2) 1.0
(4, 6) 1.0
(4, 8) 1.0
(4, 12) 1.0
(5, 2) 1.0
(5, 4) 1.0
(5, 10) 1.0
(5, 12) 1.0
(6, 2) 1.0
: :
(8, 12) 1.0
(9, 3) 1.0
(9, 4) 1.0
(9, 10) 1.0
(9, 12) 1.0
(10, 2) 1.0
(10, 6) 1.0
(10, 10) 1.0
(10, 12) 1.0
(11, 3) 1.0
(11, 6) 1.0
(11, 10) 1.0
(11, 11) 1.0
(12, 1) 1.0
(12, 6) 1.0
(12, 8) 1.0
(12, 11) 1.0
(13, 1) 1.0
(13, 5) 1.0
(13, 10) 1.0
(13, 12) 1.0
(14, 2) 1.0
(14, 6) 1.0
(14, 8) 1.0
(14, 11) 1.0
Train set accuracy with Decision Tree: 0.73
</pre></div>
</div>
<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0
</pre></div>
</div>
</div>
</div>
</div>
<div class="section" id="computing-the-gini-factor">
@@ -2147,6 +2284,67 @@ algorithm ID3.</p>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>X1 &lt; 0.000 Gini=0.408
X1 &lt; 0.000 Gini=0.408
X1 &lt; 1.000 Gini=0.394
X1 &lt; 2.000 Gini=0.394
X1 &lt; 2.000 Gini=0.394
X1 &lt; 2.000 Gini=0.394
X1 &lt; 1.000 Gini=0.394
X1 &lt; 0.000 Gini=0.408
X1 &lt; 0.000 Gini=0.408
X1 &lt; 2.000 Gini=0.394
X1 &lt; 0.000 Gini=0.408
X1 &lt; 1.000 Gini=0.394
X1 &lt; 1.000 Gini=0.394
X1 &lt; 2.000 Gini=0.394
X2 &lt; 0.000 Gini=0.408
X2 &lt; 0.000 Gini=0.408
X2 &lt; 0.000 Gini=0.408
X2 &lt; 1.000 Gini=0.407
X2 &lt; 2.000 Gini=0.407
X2 &lt; 2.000 Gini=0.407
X2 &lt; 2.000 Gini=0.407
X2 &lt; 1.000 Gini=0.407
X2 &lt; 2.000 Gini=0.407
X2 &lt; 1.000 Gini=0.407
X2 &lt; 1.000 Gini=0.407
X2 &lt; 1.000 Gini=0.407
X2 &lt; 0.000 Gini=0.408
X2 &lt; 1.000 Gini=0.407
X3 &lt; 0.000 Gini=0.408
X3 &lt; 0.000 Gini=0.408
X3 &lt; 0.000 Gini=0.408
X3 &lt; 0.000 Gini=0.408
X3 &lt; 1.000 Gini=0.367
X3 &lt; 1.000 Gini=0.367
X3 &lt; 1.000 Gini=0.367
X3 &lt; 0.000 Gini=0.408
X3 &lt; 1.000 Gini=0.367
X3 &lt; 1.000 Gini=0.367
X3 &lt; 1.000 Gini=0.367
X3 &lt; 0.000 Gini=0.408
X3 &lt; 1.000 Gini=0.367
X3 &lt; 0.000 Gini=0.408
X4 &lt; 0.000 Gini=0.408
X4 &lt; 1.000 Gini=0.405
X4 &lt; 0.000 Gini=0.408
X4 &lt; 0.000 Gini=0.408
X4 &lt; 0.000 Gini=0.408
X4 &lt; 1.000 Gini=0.405
X4 &lt; 1.000 Gini=0.405
X4 &lt; 0.000 Gini=0.408
X4 &lt; 0.000 Gini=0.408
X4 &lt; 0.000 Gini=0.408
X4 &lt; 1.000 Gini=0.405
X4 &lt; 1.000 Gini=0.405
X4 &lt; 0.000 Gini=0.408
X4 &lt; 1.000 Gini=0.405
Split: [X3 &lt; 1.000]
</pre></div>
</div>
</div>
</div>
</div>
<div class="section" id="regression-trees">
@@ -2172,6 +2370,11 @@ algorithm ID3.</p>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>DecisionTreeRegressor(max_depth=2, random_state=42)
</pre></div>
</div>
</div>
</div>
</div>
<div class="section" id="final-regressor-code">
@@ -2219,6 +2422,9 @@ algorithm ID3.</p>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
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@@ -2253,6 +2459,9 @@ algorithm ID3.</p>
</pre></div>
</div>
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<img alt="_images/week46_73_0.png" src="_images/week46_73_0.png" />
</div>
</div>
</div>
<div class="section" id="pros-and-cons-of-trees-pros">
@@ -2415,6 +2624,9 @@ numbers kicking in.</p>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<img alt="_images/week46_83_0.png" src="_images/week46_83_0.png" />
</div>
</div>
</div>
<div class="section" id="using-the-voting-classifier">
@@ -2467,6 +2679,18 @@ numbers kicking in.</p>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LogisticRegression 0.864
RandomForestClassifier 0.872
SVC 0.888
VotingClassifier 0.896
LogisticRegression 0.864
RandomForestClassifier 0.872
SVC 0.888
VotingClassifier 0.912
</pre></div>
</div>
</div>
</div>
</div>
<div class="section" id="voting-and-bagging">
@@ -2494,6 +2718,13 @@ numbers kicking in.</p>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>VotingClassifier(estimators=[(&#39;lr&#39;, LogisticRegression(random_state=42)),
(&#39;rf&#39;, RandomForestClassifier(random_state=42)),
(&#39;svc&#39;, SVC(random_state=42))])
</pre></div>
</div>
</div>
</div>
<div class="cell docutils container">
<div class="cell_input docutils container">
@@ -2506,6 +2737,16 @@ numbers kicking in.</p>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LogisticRegression 0.864
RandomForestClassifier 0.896
SVC 0.896
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>VotingClassifier 0.912
</pre></div>
</div>
</div>
</div>
<div class="cell docutils container">
<div class="cell_input docutils container">
@@ -2520,6 +2761,14 @@ numbers kicking in.</p>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>VotingClassifier(estimators=[(&#39;lr&#39;, LogisticRegression(random_state=42)),
(&#39;rf&#39;, RandomForestClassifier(random_state=42)),
(&#39;svc&#39;, SVC(probability=True, random_state=42))],
voting=&#39;soft&#39;)
</pre></div>
</div>
</div>
</div>
<div class="cell docutils container">
<div class="cell_input docutils container">
@@ -2532,6 +2781,16 @@ numbers kicking in.</p>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>LogisticRegression 0.864
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>RandomForestClassifier 0.896
SVC 0.896
VotingClassifier 0.92
</pre></div>
</div>
</div>
</div>
</div>
<div class="section" id="bagging">
@@ -2636,6 +2895,49 @@ a decision tree wth different depths and perform a bootstrap aggregate (in this
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 1
Error: 0.06380941468319971
Bias^2: 0.05160313473529168
Var: 0.01220627994790804
0.06380941468319971 &gt;= 0.05160313473529168 + 0.01220627994790804 = 0.06380941468319971
Polynomial degree: 2
Error: 0.043464037468677004
Bias^2: 0.02659851591375224
Var: 0.01686552155492476
0.043464037468677004 &gt;= 0.02659851591375224 + 0.01686552155492476 = 0.043464037468677
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Polynomial degree: 3
Error: 0.020716391693769383
Bias^2: 0.01159033914386312
Var: 0.00912605254990626
0.020716391693769383 &gt;= 0.01159033914386312 + 0.00912605254990626 = 0.02071639169376938
Polynomial degree: 4
Error: 0.02063627410934057
Bias^2: 0.0117496656370668
Var: 0.008886608472273775
0.02063627410934057 &gt;= 0.0117496656370668 + 0.008886608472273775 = 0.020636274109340574
Polynomial degree: 5
Error: 0.02087627881701288
Bias^2: 0.01349183949256158
Var: 0.007384439324451296
0.02087627881701288 &gt;= 0.01349183949256158 + 0.007384439324451296 = 0.020876278817012876
Polynomial degree: 6
Error: 0.02069601123831537
Bias^2: 0.013918526350129823
Var: 0.0067774848881855445
0.02069601123831537 &gt;= 0.013918526350129823 + 0.0067774848881855445 = 0.020696011238315368
Polynomial degree: 7
Error: 0.022964339924731444
Bias^2: 0.01550381208433455
Var: 0.007460527840396904
0.022964339924731444 &gt;= 0.01550381208433455 + 0.007460527840396904 = 0.022964339924731455
Simple tree: 0.5148389267750961
</pre></div>
</div>
<img alt="_images/week46_94_2.png" src="_images/week46_94_2.png" />
</div>
</div>
</div>
<div class="section" id="random-forests">
@@ -2756,6 +3058,23 @@ this setting.</p>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>(426, 30)
(143, 30)
Test set accuracy Logistic Regression with scaled data: 0.96
Test set accuracy SVM with scaled data: 0.96
Test set accuracy with Decision Trees and scaled data: 0.87
</pre></div>
</div>
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[0.93333333 0.73333333 0.93333333 1. 1. 0.92857143
1. 0.92857143 0.92857143 0.92857143]
Test set accuracy with Random Forests and scaled data: 0.98
</pre></div>
</div>
<img alt="_images/week46_100_2.png" src="_images/week46_100_2.png" />
<img alt="_images/week46_100_3.png" src="_images/week46_100_3.png" />
<img alt="_images/week46_100_4.png" src="_images/week46_100_4.png" />
</div>
</div>
<p>Recall that the cumulative gains curve shows the percentage of the
overall number of cases in a given category <em>gained</em> by targeting a
@@ -2787,6 +3106,11 @@ discrimination threshold is varied. It plots the true positive rate against the
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.9790209790209791
</pre></div>
</div>
</div>
</div>
</div>
<div class="section" id="boosting-a-bird-s-eye-view">
@@ -3049,6 +3373,11 @@ observations that are missed in the previous iterations.</p>
</pre></div>
</div>
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<img alt="_images/week46_161_2.png" src="_images/week46_161_2.png" />
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doc/LectureNotes/_build/jupyter_execute/exercisesweek43.ipynb
+18 -17
View File
@@ -438,13 +438,7 @@
"Learning rate = 1e-05\n",
"Lambda = 1.0\n",
"Accuracy score on data set: 0.5\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Learning rate = 1e-05\n",
"Lambda = 10.0\n",
"Accuracy score on data set: 0.5\n",
@@ -604,13 +598,7 @@
"Learning rate = 10.0\n",
"Lambda = 0.01\n",
"Accuracy score on data set: 0.5\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Learning rate = 10.0\n",
"Lambda = 0.1\n",
"Accuracy score on data set: 0.5\n",
@@ -660,7 +648,7 @@
},
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"filenames": {
"image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/exercisesweek43_26_4.png"
"image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/exercisesweek43_26_2.png"
}
},
"output_type": "display_data"
@@ -10700,7 +10688,13 @@
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" [----------------------------------------] 0.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392 "
]
@@ -18756,7 +18750,14 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Adam: Eta=0.0001, Lambda=0\n"
"Adam: Eta=0.0001, Lambda=0"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
doc/LectureNotes/_build/jupyter_execute/week42.ipynb
+60 -72
View File
@@ -1146,7 +1146,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
" return 1/(1 + np.exp(-x))\n"
]
},
@@ -1717,7 +1717,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
" return 1/(1 + np.exp(-x))\n"
]
},
@@ -1735,7 +1735,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
" return 1/(1 + np.exp(-x))\n"
]
},
@@ -1753,7 +1753,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
" return 1/(1 + np.exp(-x))\n"
]
},
@@ -1771,7 +1771,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
" return 1/(1 + np.exp(-x))\n"
]
},
@@ -1789,7 +1789,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
" return 1/(1 + np.exp(-x))\n"
]
},
@@ -1807,7 +1807,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
" return 1/(1 + np.exp(-x))\n"
]
},
@@ -1825,7 +1825,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
" return 1/(1 + np.exp(-x))\n"
]
},
@@ -1843,11 +1843,11 @@
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
" return 1/(1 + np.exp(-x))\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
" exp_term = np.exp(self.z_o)\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
" self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
]
},
@@ -1865,11 +1865,11 @@
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
" return 1/(1 + np.exp(-x))\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
" exp_term = np.exp(self.z_o)\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
" self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
]
},
@@ -1887,11 +1887,11 @@
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
" return 1/(1 + np.exp(-x))\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
" exp_term = np.exp(self.z_o)\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
" self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
]
},
@@ -1909,11 +1909,11 @@
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
" return 1/(1 + np.exp(-x))\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
" exp_term = np.exp(self.z_o)\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
" self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
]
},
@@ -1931,11 +1931,11 @@
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
" return 1/(1 + np.exp(-x))\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
" exp_term = np.exp(self.z_o)\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
" self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
]
},
@@ -1953,7 +1953,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
" return 1/(1 + np.exp(-x))\n"
]
},
@@ -1971,11 +1971,11 @@
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
" return 1/(1 + np.exp(-x))\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
" exp_term = np.exp(self.z_o)\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
" self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
]
},
@@ -1993,11 +1993,11 @@
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
" return 1/(1 + np.exp(-x))\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
" exp_term = np.exp(self.z_o)\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
" self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
]
},
@@ -2015,11 +2015,11 @@
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
" return 1/(1 + np.exp(-x))\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
" exp_term = np.exp(self.z_o)\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
" self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
]
},
@@ -2037,11 +2037,11 @@
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
" return 1/(1 + np.exp(-x))\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
" exp_term = np.exp(self.z_o)\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
" self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
]
},
@@ -2059,11 +2059,11 @@
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
" return 1/(1 + np.exp(-x))\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
" exp_term = np.exp(self.z_o)\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
" self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
]
},
@@ -2081,11 +2081,11 @@
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
" return 1/(1 + np.exp(-x))\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
" exp_term = np.exp(self.z_o)\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
" self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
]
},
@@ -2103,11 +2103,11 @@
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
" return 1/(1 + np.exp(-x))\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
" exp_term = np.exp(self.z_o)\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
" self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
]
},
@@ -2125,11 +2125,11 @@
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
" return 1/(1 + np.exp(-x))\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
" exp_term = np.exp(self.z_o)\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
" self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
]
},
@@ -2185,15 +2185,15 @@
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
" return 1/(1 + np.exp(-x))\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
" return 1/(1 + np.exp(-x))\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
" return 1/(1 + np.exp(-x))\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
" return 1/(1 + np.exp(-x))\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_8861/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_44857/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
" return 1/(1 + np.exp(-x))\n"
]
},
@@ -2791,13 +2791,7 @@
"Learning rate = 1.0\n",
"Lambda = 1e-05\n",
"Accuracy score on test set: 0.08611111111111111\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Learning rate = 1.0\n",
"Lambda = 0.0001\n",
"Accuracy score on test set: 0.10555555555555556\n",
@@ -2851,6 +2845,10 @@
"Learning rate = 10.0\n",
"Lambda = 0.001\n",
"Accuracy score on test set: 0.10555555555555556\n",
"\n",
"Learning rate = 10.0\n",
"Lambda = 0.01\n",
"Accuracy score on test set: 0.1388888888888889\n",
"\n"
]
},
@@ -2858,10 +2856,6 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Learning rate = 10.0\n",
"Lambda = 0.01\n",
"Accuracy score on test set: 0.1388888888888889\n",
"\n",
"Learning rate = 10.0\n",
"Lambda = 0.1\n",
"Accuracy score on test set: 0.11388888888888889\n",
@@ -3367,13 +3361,7 @@
"Learning rate = 10.0\n",
"Lambda = 1e-05\n",
"Accuracy score on data set: 0.5\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Learning rate = 10.0\n",
"Lambda = 0.0001\n",
"Accuracy score on data set: 0.5\n",
@@ -3435,7 +3423,7 @@
},
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# given some assumptions, make predictions about the target feature value
# (class) of unseen query instances.
# ## A Sketch of a Tree, Regression problem
#
# [See handwritten notes November 3](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2022/NotesNov32022.pdf)
#
# <!-- FIGURE: [DataFiles/Regsimpletree.png, width=600 frac=0.8] -->
# ## A Sketch of a Tree, Classification problem
#
# [See handwritten notes November 3](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2022/NotesNov32022.pdf)
# <!-- FIGURE: [DataFiles/Classimpletree.png, width=600 frac=0.8] -->
# ## A typical Decision Tree with its pertinent Jargon, Classification Problem
#
# <!-- dom:FIGURE: [DataFiles/cancer.png, width=600 frac=0.8] -->
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