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Morten Hjorth-Jensen
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Applied Data Analysis and Machine Learning, FYS-STK3155/4155 at the University of Oslo, Norway
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Applied Data Analysis and Machine Learning
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About the course
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Review of Statistics with Resampling Techniques and Linear Algebra
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From Regression to Support Vector Machines
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Decision Trees, Ensemble Methods and Boosting
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Dimensionality Reduction
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11. Basic ideas of the Principal Component Analysis (PCA)
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12. Clustering and Unsupervised Learning
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Deep Learning Methods
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12. Neural networks
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13. Neural networks
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13. Building a Feed Forward Neural Network
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14. Building a Feed Forward Neural Network
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14. Solving Differential Equations with Deep Learning
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15. Solving Differential Equations with Deep Learning
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16. Convolutional Neural Networks
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17. Recurrent neural networks: Overarching view
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<h1>Logistic Regression</h1>
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6.1. Logistic Regression
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6.2. Basics
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<a class="reference internal nav-link" href="#the-logistic-function">
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6.3. The logistic function
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<a class="reference internal nav-link" href="#examples-of-likelihood-functions-used-in-logistic-regression-and-nueral-networks">
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6.4. Examples of likelihood functions used in logistic regression and nueral networks
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<div class="tex2jax_ignore mathjax_ignore section" id="logistic-regression">
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@@ -875,11 +935,11 @@ classification.</p>
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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: 0.95
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Test set accuracy with Logistic Regression: 0.94
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Test set accuracy Logistic Regression with scaled data: 0.96
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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/MortenImac/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
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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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@@ -1019,14 +1079,14 @@ applications. This will be discussed later this semester (<a class="reference ex
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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: 0.95
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Test set accuracy with Logistic Regression: 0.94
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Test set accuracy Logistic Regression with scaled data: 0.96
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[1. 1. 1. 1. 1. 1.
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1. 1. 0.92857143 0.92857143]
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Test set accuracy with Logistic Regression and scaled data: 0.96
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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/MortenImac/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
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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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@@ -1036,9 +1096,18 @@ Please also refer to the documentation for alternative solver options:
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n_iter_i = _check_optimize_result(
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</pre></div>
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<img alt="_images/chapter4_57_2.png" src="_images/chapter4_57_2.png" />
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<img alt="_images/chapter4_57_3.png" src="_images/chapter4_57_3.png" />
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<img alt="_images/chapter4_57_4.png" src="_images/chapter4_57_4.png" />
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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">ModuleNotFoundError</span><span class="g g-Whitespace"> </span>Traceback (most recent call last)
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<span class="o"><</span><span class="n">ipython</span><span class="o">-</span><span class="nb">input</span><span class="o">-</span><span class="mi">8</span><span class="o">-</span><span class="mi">12</span><span class="n">adb44b1c20</span><span class="o">></span> <span class="ow">in</span> <span class="o"><</span><span class="n">module</span><span class="o">></span>
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<span class="g g-Whitespace"> </span><span class="mi">34</span>
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<span class="g g-Whitespace"> </span><span class="mi">35</span>
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<span class="ne">---> </span><span class="mi">36</span> <span class="kn">import</span> <span class="nn">scikitplot</span> <span class="k">as</span> <span class="nn">skplt</span>
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<span class="g g-Whitespace"> </span><span class="mi">37</span> <span class="n">y_pred</span> <span class="o">=</span> <span class="n">logreg</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_scaled</span><span class="p">)</span>
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<span class="g g-Whitespace"> </span><span class="mi">38</span> <span class="n">skplt</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">plot_confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">normalize</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<span class="ne">ModuleNotFoundError</span>: No module named 'scikitplot'
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</pre></div>
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@@ -1066,54 +1135,42 @@ Please also refer to the documentation for alternative solver options:
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