update
This commit is contained in:
Morten Hjorth-Jensen
@@ -353,6 +353,16 @@ const thebe_selector_output = ".output, .cell_output"
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Week 45, Recurrent Neural Networks
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="week46.html">
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Week 46: Decision Trees, Ensemble methods and Random Forests
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="week47.html">
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Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods and Summary of Course
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</a>
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</li>
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</ul>
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<p aria-level="2" class="caption" role="heading">
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<span class="caption-text">
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@@ -367,7 +377,12 @@ const thebe_selector_output = ".output, .cell_output"
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="project2.html">
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Project 2 on Machine Learning, deadline November 13 (Midnight)
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Project 2 on Machine Learning, deadline November 17 (Midnight)
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="project3.html">
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Project 3 on Machine Learning, deadline December 18 (midnight), 2023
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</a>
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</li>
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</ul>
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@@ -817,9 +832,9 @@ predicting the target features of query instances is as follows:</p>
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</div>
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<div class="cell_output docutils container">
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>2nd degree coefficients:
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zero power: 0.9887034589972739
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first power: -0.10518426027535331
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second power: 0.0005840075008020406
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zero power: 2.7023746599300384
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first power: 0.03407676546787885
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second power: 9.208257931205295e-06
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</pre></div>
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</div>
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<img alt="_images/chapter6_1_1.png" src="_images/chapter6_1_1.png" />
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@@ -1417,16 +1432,61 @@ humidity and weak and strong for wind.</p>
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</div>
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<div class="cell_output docutils container">
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<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
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<span class="ne">FileNotFoundError</span><span class="g g-Whitespace"> </span>Traceback (most recent call last)
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<span class="nn">Input In [6],</span> in <span class="ni"><cell line: 37></span><span class="nt">()</span>
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<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>
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<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">".png"</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s1">'png'</span><span class="p">)</span>
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<span class="ne">---> </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">"rideclass.csv"</span><span class="p">),</span><span class="s1">'r'</span><span class="p">)</span>
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<span class="g g-Whitespace"> </span><span class="mi">39</span> <span class="c1"># Read the experimental data with Pandas</span>
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<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>
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<span class="ne">FileNotFoundError</span>: [Errno 2] No such file or directory: 'DataFiles/rideclass.csv'
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span> (0, 0) 1.0
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(0, 7) 1.0
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(0, 9) 1.0
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(0, 13) 1.0
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(1, 3) 1.0
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(1, 5) 1.0
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(1, 8) 1.0
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(1, 12) 1.0
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(2, 3) 1.0
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(2, 5) 1.0
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(2, 8) 1.0
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(2, 11) 1.0
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(3, 1) 1.0
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(3, 5) 1.0
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(3, 8) 1.0
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(3, 12) 1.0
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(4, 2) 1.0
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(4, 6) 1.0
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(4, 8) 1.0
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(4, 12) 1.0
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(5, 2) 1.0
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(5, 4) 1.0
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(5, 10) 1.0
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(5, 12) 1.0
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(6, 2) 1.0
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: :
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(8, 12) 1.0
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(9, 3) 1.0
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(9, 4) 1.0
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(9, 10) 1.0
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(9, 12) 1.0
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(10, 2) 1.0
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(10, 6) 1.0
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(10, 10) 1.0
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(10, 12) 1.0
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(11, 3) 1.0
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(11, 6) 1.0
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(11, 10) 1.0
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(11, 11) 1.0
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(12, 1) 1.0
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(12, 6) 1.0
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(12, 8) 1.0
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(12, 11) 1.0
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(13, 1) 1.0
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(13, 5) 1.0
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(13, 10) 1.0
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(13, 12) 1.0
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(14, 2) 1.0
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(14, 6) 1.0
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(14, 8) 1.0
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(14, 11) 1.0
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Train set accuracy with Decision Tree: 0.73
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</pre></div>
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</div>
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<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0
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</pre></div>
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@@ -1501,6 +1561,67 @@ algorithm ID3.</p>
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</pre></div>
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</div>
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</div>
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<div class="cell_output docutils container">
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>X1 < 0.000 Gini=0.408
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X1 < 0.000 Gini=0.408
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X1 < 1.000 Gini=0.394
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X1 < 2.000 Gini=0.394
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X1 < 2.000 Gini=0.394
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X1 < 2.000 Gini=0.394
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X1 < 1.000 Gini=0.394
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X1 < 0.000 Gini=0.408
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X1 < 0.000 Gini=0.408
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X1 < 2.000 Gini=0.394
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X1 < 0.000 Gini=0.408
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X1 < 1.000 Gini=0.394
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X1 < 1.000 Gini=0.394
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X1 < 2.000 Gini=0.394
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X2 < 0.000 Gini=0.408
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X2 < 0.000 Gini=0.408
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X2 < 0.000 Gini=0.408
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X2 < 1.000 Gini=0.407
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X2 < 2.000 Gini=0.407
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X2 < 2.000 Gini=0.407
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X2 < 2.000 Gini=0.407
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X2 < 1.000 Gini=0.407
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X2 < 2.000 Gini=0.407
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X2 < 1.000 Gini=0.407
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X2 < 1.000 Gini=0.407
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X2 < 1.000 Gini=0.407
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X2 < 0.000 Gini=0.408
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X2 < 1.000 Gini=0.407
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X3 < 0.000 Gini=0.408
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X3 < 0.000 Gini=0.408
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X3 < 0.000 Gini=0.408
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X3 < 0.000 Gini=0.408
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X3 < 1.000 Gini=0.367
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X3 < 1.000 Gini=0.367
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X3 < 1.000 Gini=0.367
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X3 < 0.000 Gini=0.408
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X3 < 1.000 Gini=0.367
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X3 < 1.000 Gini=0.367
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X3 < 1.000 Gini=0.367
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X3 < 0.000 Gini=0.408
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X3 < 1.000 Gini=0.367
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X3 < 0.000 Gini=0.408
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X4 < 0.000 Gini=0.408
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X4 < 1.000 Gini=0.405
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X4 < 0.000 Gini=0.408
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X4 < 0.000 Gini=0.408
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X4 < 0.000 Gini=0.408
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X4 < 1.000 Gini=0.405
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X4 < 1.000 Gini=0.405
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X4 < 0.000 Gini=0.408
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X4 < 0.000 Gini=0.408
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X4 < 0.000 Gini=0.408
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X4 < 1.000 Gini=0.405
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X4 < 1.000 Gini=0.405
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X4 < 0.000 Gini=0.408
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X4 < 1.000 Gini=0.405
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Split: [X3 < 1.000]
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</pre></div>
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</div>
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</div>
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</div>
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@@ -1573,6 +1694,32 @@ attributes at each step while growing the tree.</p>
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</pre></div>
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</div>
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</div>
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<div class="cell_output docutils container">
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>(426, 30)
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(143, 30)
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Test set accuracy with Logistic Regression: 0.94
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Test set accuracy with SVM: 0.63
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</pre></div>
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</div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Test set accuracy with Decision Trees: 0.90
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Test set accuracy Logistic Regression with scaled data: 0.96
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Test set accuracy SVM with scaled data: 0.96
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</pre></div>
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</div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Test set accuracy with Decision Trees and scaled data: 0.89
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</pre></div>
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</div>
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<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
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STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
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Increase the number of iterations (max_iter) or scale the data as shown in:
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https://scikit-learn.org/stable/modules/preprocessing.html
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Please also refer to the documentation for alternative solver options:
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https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
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n_iter_i = _check_optimize_result(
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</pre></div>
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<div class="section" id="another-example-the-moons-again">
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@@ -1646,6 +1793,9 @@ attributes at each step while growing the tree.</p>
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</pre></div>
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<img alt="_images/chapter6_42_0.png" src="_images/chapter6_42_0.png" />
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@@ -1672,6 +1822,9 @@ attributes at each step while growing the tree.</p>
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<img alt="_images/chapter6_43_0.png" src="_images/chapter6_43_0.png" />
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@@ -1694,6 +1847,11 @@ attributes at each step while growing the tree.</p>
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<div class="cell_output docutils container">
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<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>DecisionTreeRegressor(max_depth=2, random_state=42)
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</pre></div>
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@@ -1738,6 +1896,9 @@ attributes at each step while growing the tree.</p>
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</pre></div>
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<img alt="_images/chapter6_46_0.png" src="_images/chapter6_46_0.png" />
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@@ -1772,6 +1933,9 @@ attributes at each step while growing the tree.</p>
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<img alt="_images/chapter6_47_0.png" src="_images/chapter6_47_0.png" />
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