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@@ -289,6 +289,11 @@ const thebe_selector_output = ".output, .cell_output"
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Exercises week 38
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Data Analysis and Machine Learning: Logistic Regression and Optimization
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@@ -290,6 +290,11 @@ const thebe_selector_output = ".output, .cell_output"
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Exercises week 38
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Data Analysis and Machine Learning: Logistic Regression and Optimization
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@@ -295,6 +295,11 @@ const thebe_selector_output = ".output, .cell_output"
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Exercises week 38
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Data Analysis and Machine Learning: Logistic Regression and Optimization
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@@ -55,6 +55,8 @@ const thebe_selector_output = ".output, .cell_output"
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<script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
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<link rel="next" title="Project 1 on Machine Learning, deadline October 9 (midnight), 2023" href="project1.html" />
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@@ -245,7 +247,7 @@ const thebe_selector_output = ".output, .cell_output"
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Weekly material, notes and exercises
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<a class="reference internal" href="exercisesweek34.html">
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Exercises week 34
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@@ -286,6 +288,16 @@ const thebe_selector_output = ".output, .cell_output"
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Week 37: Statitsitcal interpretations and Resampling Methods
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<a class="reference internal" href="exercisesweek38.html">
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Exercises week 38
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<a class="current reference internal" href="#">
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Data Analysis and Machine Learning: Logistic Regression and Optimization
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@@ -1982,7 +1994,7 @@ case under study.</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>RandomizedSearchCV(estimator=Ridge(), n_iter=100,
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param_distributions={'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11e02eaf0>})
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param_distributions={'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x130057e80>})
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Best estimated lambda-value: 0.9849967686928113
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MSE score: 1.0853136633465326
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R2 score: -0.0002382102844775691
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@@ -2880,7 +2892,21 @@ minimum of this function.</p>
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@@ -1679,7 +1679,7 @@
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"output_type": "stream",
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"text": [
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"RandomizedSearchCV(estimator=Ridge(), n_iter=100,\n",
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" param_distributions={'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x11e02eaf0>})\n",
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" param_distributions={'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x130057e80>})\n",
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"Best estimated lambda-value: 0.9849967686928113\n",
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"MSE score: 1.0853136633465326\n",
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"R2 score: -0.0002382102844775691\n"
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