updating typos
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@@ -1646,7 +1646,7 @@ 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>0.996535469511469
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.9952714635635723
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</pre></div>
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@@ -1663,7 +1663,7 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
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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>0.0077750600806588575
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.009039197557276654
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</pre></div>
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@@ -1678,23 +1678,31 @@ Since we are not using <strong>Scikit-Learn</strong> here we can define our own
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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>[0.05751737 0.02644748 0.02533184 0.02193035 0.02521356 0.00712899
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0.00857688 0.0101339 0.01097227 0.01132947 0.01644452 0.01655455
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0.00771046 0.05177021 0.01374437 0.04208274 0.01667873 0.00228986
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0.00749579 0.00852965 0.01628823 0.0341899 0.01178253 0.00626339
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0.00514913 0.00791019 0.00363986 0.00471966 0.00390805 0.00910241
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0.00232255 0.02670606 0.03224523 0.01637147 0.00918914 0.02705154
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0.00321694 0.01904583 0.0177181 0.00538287 0.02291121 0.01028255
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0.04566103 0.00954257 0.00011551 0.0283589 0.00953728 0.03399521
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0.01205286 0.02251524 0.00353655 0.02344605 0.05288681 0.01950574
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0.00330907 0.01492855 0.01472766 0.03952093 0.01491299 0.00187154
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0.02897168 0.00072037 0.00895315 0.02775293 0.01547992 0.04117184
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0.0116726 0.03330335 0.01515966 0.0070381 0.01317074 0.00654702
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0.00329264 0.02502101 0.00226569 0.05029894 0.01929643 0.03028379
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0.053994 0.03413302 0.01854824 0.00393744 0.0600658 0.01855624
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0.0590702 0.01826743 0.01039549 0.02151219 0.00928016 0.03536062
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0.00503218 0.08526352 0.00506765 0.02609885 0.04771105 0.0010059
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0.00659545 0.00143188 0.01489692 0.08237141]
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[6.11430166e-02 7.59428035e-03 7.64295978e-03 5.34442654e-02
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2.53861506e-02 7.46543658e-02 2.79339933e-02 1.32946226e-02
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3.80320028e-03 2.40474117e-02 4.36028477e-02 3.24382528e-02
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1.26022420e-02 5.06848116e-03 1.96793569e-02 1.04826413e-04
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9.25580178e-03 1.80413812e-02 1.22519792e-02 1.01505087e-02
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2.67180442e-02 6.71316703e-02 7.68415876e-03 3.33174939e-02
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1.43570786e-02 8.43459933e-03 4.11046407e-03 9.10453697e-04
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4.33142087e-03 7.64507671e-02 2.19696497e-02 1.18460525e-02
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4.35665906e-02 3.62818572e-02 1.61018347e-03 3.78738205e-02
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4.57031517e-03 3.11279984e-02 1.07745809e-02 7.09848429e-03
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2.30006341e-03 4.62535124e-02 6.70493970e-02 6.26624267e-03
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4.23399553e-02 1.88690267e-02 7.46184816e-03 1.75934097e-02
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3.60820168e-02 1.09322328e-02 3.16749353e-02 2.30626515e-02
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5.76196895e-04 1.51000420e-02 1.95456434e-03 1.22138768e-02
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2.52945186e-03 2.12804129e-03 3.32695433e-03 1.90886410e-02
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1.84291049e-02 1.78104004e-02 6.34498455e-02 1.50032818e-02
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3.43525003e-02 1.52033394e-03 3.95921014e-02 7.67148008e-02
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2.40893740e-02 6.75638425e-02 1.22105472e-02 7.72706640e-03
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3.00721135e-02 8.77081499e-02 1.40684484e-02 4.57081718e-02
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3.51190686e-02 7.42092776e-02 4.03105744e-02 1.08994018e-02
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1.37071826e-02 3.69106251e-02 1.94966662e-03 1.71112140e-02
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3.12669602e-03 3.01610677e-02 1.72139847e-02 2.75703556e-02
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2.23333773e-02 2.76892410e-03 8.05886306e-02 1.15545946e-01
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4.88029792e-02 4.59648068e-02 1.98750794e-02 2.69178261e-03
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1.07925220e-02 1.13151043e-03 5.00620642e-03 3.69871367e-03]
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</pre></div>
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</div>
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</div>
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@@ -1763,15 +1771,15 @@ but now splitting the data into a training set and a test set.</p>
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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>[ 1.95976303 0.48974963 2.82202765 3.79224555 -2.14248744]
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 2.04038959 -0.81343612 7.94304103 -3.83770135 1.70073443]
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Training R2
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0.9947613223847728
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0.9954441212986554
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Training MSE
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0.010596520060222926
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0.008653750612762521
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Test R2
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0.989645675273948
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0.99255249100346
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Test MSE
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0.01889099091713886
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0.016970019460234076
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</pre></div>
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</div>
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</div>
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