[ci skip] minor improvements in wording and import statement order (#841)

* minor improvment in wording and import statement order

* use pre-commit hooks 1c40b1c

Author: ogrisel

_sources/python_scripts/linear_models_ex_02.py

@@ -30,7 +30,7 @@
 # disease. This would depend on the patient's Body Mass Index which is defined
 # as `weight / height ** 2`.
 #
-# We load the dataset penguins dataset. We first use a set of 3 numerical
+# We load the penguins dataset. We first use a set of 3 numerical
 # features to predict the target, i.e. the body mass of the penguin.
 
 # %% [markdown]

_sources/python_scripts/linear_models_sol_02.py

@@ -24,7 +24,7 @@
 # disease. This would depend on the patient's Body Mass Index which is defined
 # as `weight / height ** 2`.
 #
-# We load the dataset penguins dataset. We first use a set of 3 numerical
+# We load the penguins dataset. We first use a set of 3 numerical
 # features to predict the target, i.e. the body mass of the penguin.
 
 # %% [markdown]

_sources/python_scripts/linear_regression_non_linear_link.py

@@ -76,6 +76,9 @@
 
 
 # %%
+from sklearn.metrics import mean_squared_error
+
+
 def fit_score_plot_regression(model, title=None):
     model.fit(data, target)
     target_predicted = model.predict(data)
@@ -95,7 +98,6 @@ def fit_score_plot_regression(model, title=None):
 
 # %%
 from sklearn.linear_model import LinearRegression
-from sklearn.metrics import mean_squared_error
 
 linear_regression = LinearRegression()
 linear_regression

appendix/notebook_timings.html

@@ -1073,9 +1073,9 @@ <h1>Notebook timings<a class="headerlink" href="#notebook-timings" title="Link t
 <td><p>✅</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="xref doc reference internal" href="../python_scripts/linear_regression_non_linear_link.html"><span class="doc">python_scripts/linear_regression_non_linear_link</span></a></p></td>
-<td><p>2025-05-09 15:40</p></td>
+<td><p>2025-06-02 13:58</p></td>
 <td><p>cache</p></td>
-<td><p>3.79</p></td>
+<td><p>5.21</p></td>
 <td><p>✅</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="xref doc reference internal" href="../python_scripts/linear_regression_without_sklearn.html"><span class="doc">python_scripts/linear_regression_without_sklearn</span></a></p></td>

python_scripts/linear_models_ex_02.html

@@ -710,7 +710,7 @@ <h1>📝 Exercise M4.02<a class="headerlink" href="#exercise-m4-02" title="Link
 classification model to decide if a patient has risk of developing a heart
 disease. This would depend on the patient’s Body Mass Index which is defined
 as <code class="docutils literal notranslate"><span class="pre">weight</span> <span class="pre">/</span> <span class="pre">height</span> <span class="pre">**</span> <span class="pre">2</span></code>.</p>
-<p>We load the dataset penguins dataset. We first use a set of 3 numerical
+<p>We load the penguins dataset. We first use a set of 3 numerical
 features to predict the target, i.e. the body mass of the penguin.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>

python_scripts/linear_models_sol_02.html

@@ -710,7 +710,7 @@ <h1>📃 Solution for Exercise M4.02<a class="headerlink" href="#solution-for-ex
 classification model to decide if a patient has risk of developing a heart
 disease. This would depend on the patient’s Body Mass Index which is defined
 as <code class="docutils literal notranslate"><span class="pre">weight</span> <span class="pre">/</span> <span class="pre">height</span> <span class="pre">**</span> <span class="pre">2</span></code>.</p>
-<p>We load the dataset penguins dataset. We first use a set of 3 numerical
+<p>We load the penguins dataset. We first use a set of 3 numerical
 features to predict the target, i.e. the body mass of the penguin.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>

python_scripts/linear_regression_non_linear_link.html

@@ -791,7 +791,10 @@ <h1>Non-linear feature engineering for Linear Regression<a class="headerlink" hr
 that fits, scores and plots the different regression models.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">fit_score_plot_regression</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">mean_squared_error</span>
+
+
+<span class="k">def</span><span class="w"> </span><span class="nf">fit_score_plot_regression</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
     <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
     <span class="n">target_predicted</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
     <span class="n">mse</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">target_predicted</span><span class="p">)</span>
@@ -811,7 +814,6 @@ <h1>Non-linear feature engineering for Linear Regression<a class="headerlink" hr
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LinearRegression</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">mean_squared_error</span>
 
 <span class="n">linear_regression</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span>
 <span class="n">linear_regression</span>