[ci skip] MTN Exercise M4.01 target correction (#871) 1ee99d0

Author: ArturoAmorQ

_sources/python_scripts/linear_models_ex_01.py

@@ -40,7 +40,7 @@
 penguins = pd.read_csv("../datasets/penguins_regression.csv")
 feature_name = "Flipper Length (mm)"
 target_name = "Body Mass (g)"
-data, target = penguins[[feature_name]], penguins[target_name]
+data, target = penguins[[feature_name]], penguins[[target_name]]
 
 # %% [markdown]
 # ### Model definition

_sources/python_scripts/linear_models_sol_01.py

@@ -34,7 +34,7 @@
 penguins = pd.read_csv("../datasets/penguins_regression.csv")
 feature_name = "Flipper Length (mm)"
 target_name = "Body Mass (g)"
-data, target = penguins[[feature_name]], penguins[target_name]
+data, target = penguins[[feature_name]], penguins[[target_name]]
 
 # %% [markdown]
 # ### Model definition
@@ -106,7 +106,7 @@ def linear_model_flipper_mass(
 def goodness_fit_measure(true_values, predictions):
     # we compute the error between the true values and the predictions of our
     # model
-    errors = np.ravel(true_values) - np.ravel(predictions)
+    errors = true_values - predictions
     # We have several possible strategies to reduce all errors to a single value.
     # Computing the mean error (sum divided by the number of element) might seem
     # like a good solution. However, we have negative errors that will misleadingly

appendix/notebook_timings.html

@@ -1007,9 +1007,9 @@ <h1>Notebook timings<a class="headerlink" href="#notebook-timings" title="Link t
 <td><p>✅</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="xref doc reference internal" href="../python_scripts/linear_models_ex_01.html"><span class="doc">python_scripts/linear_models_ex_01</span></a></p></td>
-<td><p>2026-01-15 15:37</p></td>
+<td><p>2026-01-19 09:44</p></td>
 <td><p>cache</p></td>
-<td><p>1.31</p></td>
+<td><p>1.45</p></td>
 <td><p>✅</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="xref doc reference internal" href="../python_scripts/linear_models_ex_02.html"><span class="doc">python_scripts/linear_models_ex_02</span></a></p></td>
@@ -1043,9 +1043,9 @@ <h1>Notebook timings<a class="headerlink" href="#notebook-timings" title="Link t
 <td><p>✅</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="xref doc reference internal" href="../python_scripts/linear_models_sol_01.html"><span class="doc">python_scripts/linear_models_sol_01</span></a></p></td>
-<td><p>2026-01-15 15:37</p></td>
+<td><p>2026-01-19 09:44</p></td>
 <td><p>cache</p></td>
-<td><p>2.29</p></td>
+<td><p>2.86</p></td>
 <td><p>✅</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="xref doc reference internal" href="../python_scripts/linear_models_sol_02.html"><span class="doc">python_scripts/linear_models_sol_02</span></a></p></td>

python_scripts/linear_models_ex_01.html

@@ -738,7 +738,7 @@ <h3>Data loading<a class="headerlink" href="#data-loading" title="Link to this h
 <span class="n">penguins</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;../datasets/penguins_regression.csv&quot;</span><span class="p">)</span>
 <span class="n">feature_name</span> <span class="o">=</span> <span class="s2">&quot;Flipper Length (mm)&quot;</span>
 <span class="n">target_name</span> <span class="o">=</span> <span class="s2">&quot;Body Mass (g)&quot;</span>
-<span class="n">data</span><span class="p">,</span> <span class="n">target</span> <span class="o">=</span> <span class="n">penguins</span><span class="p">[[</span><span class="n">feature_name</span><span class="p">]],</span> <span class="n">penguins</span><span class="p">[</span><span class="n">target_name</span><span class="p">]</span>
+<span class="n">data</span><span class="p">,</span> <span class="n">target</span> <span class="o">=</span> <span class="n">penguins</span><span class="p">[[</span><span class="n">feature_name</span><span class="p">]],</span> <span class="n">penguins</span><span class="p">[[</span><span class="n">target_name</span><span class="p">]]</span>
 </pre></div>
 </div>
 </div>

python_scripts/linear_models_sol_01.html

@@ -738,7 +738,7 @@ <h3>Data loading<a class="headerlink" href="#data-loading" title="Link to this h
 <span class="n">penguins</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;../datasets/penguins_regression.csv&quot;</span><span class="p">)</span>
 <span class="n">feature_name</span> <span class="o">=</span> <span class="s2">&quot;Flipper Length (mm)&quot;</span>
 <span class="n">target_name</span> <span class="o">=</span> <span class="s2">&quot;Body Mass (g)&quot;</span>
-<span class="n">data</span><span class="p">,</span> <span class="n">target</span> <span class="o">=</span> <span class="n">penguins</span><span class="p">[[</span><span class="n">feature_name</span><span class="p">]],</span> <span class="n">penguins</span><span class="p">[</span><span class="n">target_name</span><span class="p">]</span>
+<span class="n">data</span><span class="p">,</span> <span class="n">target</span> <span class="o">=</span> <span class="n">penguins</span><span class="p">[[</span><span class="n">feature_name</span><span class="p">]],</span> <span class="n">penguins</span><span class="p">[[</span><span class="n">target_name</span><span class="p">]]</span>
 </pre></div>
 </div>
 </div>
@@ -823,7 +823,7 @@ <h2>Main exercise<a class="headerlink" href="#main-exercise" title="Link to this
 <span class="k">def</span><span class="w"> </span><span class="nf">goodness_fit_measure</span><span class="p">(</span><span class="n">true_values</span><span class="p">,</span> <span class="n">predictions</span><span class="p">):</span>
     <span class="c1"># we compute the error between the true values and the predictions of our</span>
     <span class="c1"># model</span>
-    <span class="n">errors</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">true_values</span><span class="p">)</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">predictions</span><span class="p">)</span>
+    <span class="n">errors</span> <span class="o">=</span> <span class="n">true_values</span> <span class="o">-</span> <span class="n">predictions</span>
     <span class="c1"># We have several possible strategies to reduce all errors to a single value.</span>
     <span class="c1"># Computing the mean error (sum divided by the number of element) might seem</span>
     <span class="c1"># like a good solution. However, we have negative errors that will misleadingly</span>
@@ -859,15 +859,15 @@ <h2>Main exercise<a class="headerlink" href="#main-exercise" title="Link to this
 <div class="cell_output docutils container">
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Model #0:
 -40.00 (g / mm) * flipper length + 15000.00 (g)
-Error: 2764.854
+Error: nan
 
 Model #1:
 45.00 (g / mm) * flipper length + -5000.00 (g)
-Error: 338.523
+Error: nan
 
 Model #2:
 90.00 (g / mm) * flipper length + -14000.00 (g)
-Error: 573.041
+Error: nan
 </pre></div>
 </div>
 </div>