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Morten Hjorth-Jensen
2024-10-28 06:15:26 +01:00
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@@ -348,6 +348,11 @@ const thebe_selector_output = ".output, .cell_output"
Exercises week 43
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<a class="reference internal" href="week44.html">
Week 44, Convolutional Neural Networks (CNN)
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@@ -1696,7 +1701,7 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.9951746722640107
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.9960309859796598
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@@ -1713,7 +1718,7 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.011011355570628998
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.009724998242604404
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@@ -1728,23 +1733,23 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[0.04642309 0.0279278 0.0228686 0.0650014 0.01290661 0.01519201
0.0016396 0.05684345 0.06249397 0.00778835 0.00518242 0.02159357
0.00421124 0.01267244 0.02280587 0.00184147 0.07626658 0.02041607
0.03238421 0.02937931 0.04215521 0.03179975 0.00508636 0.04983222
0.0705828 0.00555576 0.02882718 0.00046916 0.00861192 0.04145287
0.02255993 0.00567103 0.02731257 0.02307403 0.02643023 0.03374269
0.02728257 0.00045414 0.01269348 0.01433606 0.0031986 0.00813523
0.01677512 0.02132304 0.02971554 0.02671209 0.03110579 0.00701382
0.0281646 0.01167316 0.00049905 0.01368052 0.01667926 0.00935737
0.02496143 0.08648837 0.01394224 0.04394742 0.00582904 0.06185995
0.03162926 0.05276967 0.01028953 0.05735741 0.01571214 0.02287532
0.01947663 0.00861563 0.00414684 0.00858857 0.0036035 0.00523549
0.01608796 0.03520118 0.02959231 0.00068056 0.02717123 0.02989838
0.01203668 0.02622061 0.0109944 0.09258408 0.03607387 0.01775486
0.08931681 0.00514007 0.03450725 0.03465317 0.01712306 0.00757359
0.02069038 0.02243626 0.01508864 0.03202289 0.06776056 0.01054406
0.0169931 0.01784495 0.00962196 0.03370657]
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[0.04358642 0.01141029 0.0435614 0.00703349 0.05933848 0.04644334
0.00355583 0.04946218 0.06473841 0.01522194 0.02492839 0.01331377
0.03460821 0.04249722 0.03803121 0.01330851 0.0209366 0.04740225
0.01287575 0.02432892 0.0212243 0.02026696 0.01956208 0.06172974
0.00781304 0.02535601 0.02299139 0.00642673 0.06636179 0.01543463
0.00485699 0.04007138 0.01269131 0.01207585 0.05093545 0.10440237
0.05140098 0.04050007 0.00041514 0.01136346 0.01146165 0.00545269
0.02308971 0.06033348 0.05777676 0.01621264 0.03982843 0.00664787
0.04863731 0.02324012 0.08188888 0.00918064 0.01879279 0.00590621
0.01493602 0.02972907 0.02201916 0.01724416 0.00774514 0.02101997
0.00960705 0.02180068 0.00078941 0.00684494 0.00135206 0.00448
0.02412606 0.00767649 0.10848476 0.00013622 0.04684669 0.03330946
0.02565627 0.01196444 0.02901384 0.01765696 0.00550901 0.00408609
0.01399696 0.00851785 0.01518425 0.01147217 0.03078393 0.02034322
0.03762405 0.07153605 0.00778706 0.02160067 0.00577307 0.02272294
0.06726755 0.00914684 0.02512704 0.0452313 0.01120918 0.00652804
0.02085067 0.02378634 0.02560435 0.01323492]
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@@ -1813,15 +1818,15 @@ but now splitting the data into a training set and a test set.</p>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 1.99804178 -0.16533342 5.68321093 -0.84401704 0.35781308]
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 2.014896 -0.32009434 6.3804687 -1.81093622 0.74663164]
Training R2
0.9960664320362111
0.9955259363342327
Training MSE
0.008377169630073601
0.00915723745398148
Test R2
0.9944299733827195
0.9941130198635889
Test MSE
0.01282028141098116
0.009410131112671444
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