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<section id="partial-dependence-and-individual-conditional-expectation-plots">
<span id="partial-dependence"></span><h1><span class="section-number">4.1. </span>Partial Dependence and Individual Conditional Expectation plots<a class="headerlink" href="#partial-dependence-and-individual-conditional-expectation-plots" title="Link to this heading">#</a></h1>
<p>Partial dependence plots (PDP) and individual conditional expectation (ICE)
plots can be used to visualize and analyze interaction between the target
response <a class="footnote-reference brackets" href="#id6" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> and a set of input features of interest.</p>
<p>Both PDPs <a class="reference internal" href="#h2009" id="id2"><span>[H2009]</span></a> and ICEs <a class="reference internal" href="#g2015" id="id3"><span>[G2015]</span></a> assume that the input features of interest
are independent from the complement features, and this assumption is often
violated in practice. Thus, in the case of correlated features, we will
create absurd data points to compute the PDP/ICE <a class="reference internal" href="#m2019" id="id4"><span>[M2019]</span></a>.</p>
<section id="partial-dependence-plots">
<h2><span class="section-number">4.1.1. </span>Partial dependence plots<a class="headerlink" href="#partial-dependence-plots" title="Link to this heading">#</a></h2>
<p>Partial dependence plots (PDP) show the dependence between the target response
and a set of input features of interest, marginalizing over the values
of all other input features (the ‘complement’ features). Intuitively, we can
interpret the partial dependence as the expected target response as a
function of the input features of interest.</p>
<p>Due to the limits of human perception, the size of the set of input features of
interest must be small (usually, one or two) thus the input features of interest
are usually chosen among the most important features.</p>
<p>The figure below shows two one-way and one two-way partial dependence plots for
the bike sharing dataset, with a
<a class="reference internal" href="generated/sklearn.ensemble.HistGradientBoostingRegressor.html#sklearn.ensemble.HistGradientBoostingRegressor" title="sklearn.ensemble.HistGradientBoostingRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">HistGradientBoostingRegressor</span></code></a>:</p>
<figure class="align-center">
<a class="reference external image-reference" href="../auto_examples/inspection/plot_partial_dependence.html"><img alt="../_images/sphx_glr_plot_partial_dependence_006.png" src="../_images/sphx_glr_plot_partial_dependence_006.png" style="width: 700.0px; height: 280.0px;" />
</a>
</figure>
<p>One-way PDPs tell us about the interaction between the target response and an input
feature of interest (e.g. linear, non-linear). The left plot in the above figure
shows the effect of the temperature on the number of bike rentals; we can clearly see
that a higher temperature is related with a higher number of bike rentals. Similarly, we
could analyze the effect of the humidity on the number of bike rentals (middle plot).
Thus, these interpretations are marginal, considering a feature at a time.</p>
<p>PDPs with two input features of interest show the interactions among the two features.
For example, the two-variable PDP in the above figure shows the dependence of the number
of bike rentals on joint values of temperature and humidity. We can clearly see an
interaction between the two features: with a temperature higher than 20 degrees Celsius,
mainly the humidity has a strong impact on the number of bike rentals. For lower
temperatures, both the temperature and the humidity have an impact on the number of bike
rentals.</p>
<p>The <a class="reference internal" href="../api/sklearn.inspection.html#module-sklearn.inspection" title="sklearn.inspection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.inspection</span></code></a> module provides a convenience function
<a class="reference internal" href="generated/sklearn.inspection.PartialDependenceDisplay.html#sklearn.inspection.PartialDependenceDisplay.from_estimator" title="sklearn.inspection.PartialDependenceDisplay.from_estimator"><code class="xref py py-func docutils literal notranslate"><span class="pre">from_estimator</span></code></a> to create one-way and two-way partial
dependence plots. In the below example we show how to create a grid of
partial dependence plots: two one-way PDPs for the features <code class="docutils literal notranslate"><span class="pre">0</span></code> and <code class="docutils literal notranslate"><span class="pre">1</span></code>
and a two-way PDP between the two features:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_hastie_10_2</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">GradientBoostingClassifier</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.inspection</span> <span class="kn">import</span> <span class="n">PartialDependenceDisplay</span>
<span class="gp">>>> </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_hastie_10_2</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">clf</span> <span class="o">=</span> <span class="n">GradientBoostingClassifier</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">max_depth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">features</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)]</span>
<span class="gp">>>> </span><span class="n">PartialDependenceDisplay</span><span class="o">.</span><span class="n">from_estimator</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">features</span><span class="p">)</span>
<span class="go"><...></span>
</pre></div>
</div>
<p>You can access the newly created figure and Axes objects using <code class="docutils literal notranslate"><span class="pre">plt.gcf()</span></code>
and <code class="docutils literal notranslate"><span class="pre">plt.gca()</span></code>.</p>
<p>To make a partial dependence plot with categorical features, you need to specify
which features are categorical using the parameter <code class="docutils literal notranslate"><span class="pre">categorical_features</span></code>. This
parameter takes a list of indices, names of the categorical features or a boolean
mask. The graphical representation of partial dependence for categorical features is
a bar plot or a 2D heatmap.</p>
<details class="sd-sphinx-override sd-dropdown sd-card sd-mb-3" id="pdps-for-multi-class-classification">
<summary class="sd-summary-title sd-card-header">
<span class="sd-summary-text">PDPs for multi-class classification<a class="headerlink" href="#pdps-for-multi-class-classification" title="Link to this dropdown">#</a></span><span class="sd-summary-state-marker sd-summary-chevron-right"><svg version="1.1" width="1.5em" height="1.5em" class="sd-octicon sd-octicon-chevron-right" viewBox="0 0 24 24" aria-hidden="true"><path d="M8.72 18.78a.75.75 0 0 1 0-1.06L14.44 12 8.72 6.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018l6.25 6.25a.75.75 0 0 1 0 1.06l-6.25 6.25a.75.75 0 0 1-1.06 0Z"></path></svg></span></summary><div class="sd-summary-content sd-card-body docutils">
<p class="sd-card-text">For multi-class classification, you need to set the class label for which
the PDPs should be created via the <code class="docutils literal notranslate"><span class="pre">target</span></code> argument:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_iris</span>
<span class="gp">>>> </span><span class="n">iris</span> <span class="o">=</span> <span class="n">load_iris</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">mc_clf</span> <span class="o">=</span> <span class="n">GradientBoostingClassifier</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">max_depth</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">iris</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">iris</span><span class="o">.</span><span class="n">target</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">features</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">)]</span>
<span class="gp">>>> </span><span class="n">PartialDependenceDisplay</span><span class="o">.</span><span class="n">from_estimator</span><span class="p">(</span><span class="n">mc_clf</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">features</span><span class="p">,</span> <span class="n">target</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="go"><...></span>
</pre></div>
</div>
<p class="sd-card-text">The same parameter <code class="docutils literal notranslate"><span class="pre">target</span></code> is used to specify the target in multi-output
regression settings.</p>
</div>
</details><p>If you need the raw values of the partial dependence function rather than
the plots, you can use the
<a class="reference internal" href="generated/sklearn.inspection.partial_dependence.html#sklearn.inspection.partial_dependence" title="sklearn.inspection.partial_dependence"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.inspection.partial_dependence</span></code></a> function:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.inspection</span> <span class="kn">import</span> <span class="n">partial_dependence</span>
<span class="gp">>>> </span><span class="n">results</span> <span class="o">=</span> <span class="n">partial_dependence</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">results</span><span class="p">[</span><span class="s2">"average"</span><span class="p">]</span>
<span class="go">array([[ 2.466..., 2.466..., ...</span>
<span class="gp">>>> </span><span class="n">results</span><span class="p">[</span><span class="s2">"grid_values"</span><span class="p">]</span>
<span class="go">[array([-1.624..., -1.592..., ...</span>
</pre></div>
</div>
<p>The values at which the partial dependence should be evaluated are directly
generated from <code class="docutils literal notranslate"><span class="pre">X</span></code>. For 2-way partial dependence, a 2D-grid of values is
generated. The <code class="docutils literal notranslate"><span class="pre">values</span></code> field returned by
<a class="reference internal" href="generated/sklearn.inspection.partial_dependence.html#sklearn.inspection.partial_dependence" title="sklearn.inspection.partial_dependence"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.inspection.partial_dependence</span></code></a> gives the actual values
used in the grid for each input feature of interest. They also correspond to
the axis of the plots.</p>
</section>
<section id="individual-conditional-expectation-ice-plot">
<span id="individual-conditional"></span><h2><span class="section-number">4.1.2. </span>Individual conditional expectation (ICE) plot<a class="headerlink" href="#individual-conditional-expectation-ice-plot" title="Link to this heading">#</a></h2>
<p>Similar to a PDP, an individual conditional expectation (ICE) plot
shows the dependence between the target function and an input feature of
interest. However, unlike a PDP, which shows the average effect of the input
feature, an ICE plot visualizes the dependence of the prediction on a
feature for each sample separately with one line per sample.
Due to the limits of human perception, only one input feature of interest is
supported for ICE plots.</p>
<p>The figures below show two ICE plots for the bike sharing dataset,
with a <a class="reference internal" href="generated/sklearn.ensemble.HistGradientBoostingRegressor.html#sklearn.ensemble.HistGradientBoostingRegressor" title="sklearn.ensemble.HistGradientBoostingRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">HistGradientBoostingRegressor</span></code></a>:.
The figures plot the corresponding PD line overlaid on ICE lines.</p>
<figure class="align-center">
<a class="reference external image-reference" href="../auto_examples/inspection/plot_partial_dependence.html"><img alt="../_images/sphx_glr_plot_partial_dependence_004.png" src="../_images/sphx_glr_plot_partial_dependence_004.png" style="width: 420.0px; height: 280.0px;" />
</a>
</figure>
<p>While the PDPs are good at showing the average effect of the target features,
they can obscure a heterogeneous relationship created by interactions.
When interactions are present the ICE plot will provide many more insights.
For example, we see that the ICE for the temperature feature gives us some
additional information: Some of the ICE lines are flat while some others
shows a decrease of the dependence for temperature above 35 degrees Celsius.
We observe a similar pattern for the humidity feature: some of the ICE
lines show a sharp decrease when the humidity is above 80%.</p>
<p>The <a class="reference internal" href="../api/sklearn.inspection.html#module-sklearn.inspection" title="sklearn.inspection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.inspection</span></code></a> module’s <a class="reference internal" href="generated/sklearn.inspection.PartialDependenceDisplay.html#sklearn.inspection.PartialDependenceDisplay.from_estimator" title="sklearn.inspection.PartialDependenceDisplay.from_estimator"><code class="xref py py-meth docutils literal notranslate"><span class="pre">PartialDependenceDisplay.from_estimator</span></code></a>
convenience function can be used to create ICE plots by setting
<code class="docutils literal notranslate"><span class="pre">kind='individual'</span></code>. In the example below, we show how to create a grid of
ICE plots:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_hastie_10_2</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">GradientBoostingClassifier</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.inspection</span> <span class="kn">import</span> <span class="n">PartialDependenceDisplay</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_hastie_10_2</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">clf</span> <span class="o">=</span> <span class="n">GradientBoostingClassifier</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">learning_rate</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">max_depth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">features</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">PartialDependenceDisplay</span><span class="o">.</span><span class="n">from_estimator</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">features</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">kind</span><span class="o">=</span><span class="s1">'individual'</span><span class="p">)</span>
<span class="go"><...></span>
</pre></div>
</div>
<p>In ICE plots it might not be easy to see the average effect of the input
feature of interest. Hence, it is recommended to use ICE plots alongside
PDPs. They can be plotted together with
<code class="docutils literal notranslate"><span class="pre">kind='both'</span></code>.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">PartialDependenceDisplay</span><span class="o">.</span><span class="n">from_estimator</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">features</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">kind</span><span class="o">=</span><span class="s1">'both'</span><span class="p">)</span>
<span class="go"><...></span>
</pre></div>
</div>
<p>If there are too many lines in an ICE plot, it can be difficult to see
differences between individual samples and interpret the model. Centering the
ICE at the first value on the x-axis, produces centered Individual Conditional
Expectation (cICE) plots <a class="reference internal" href="#g2015" id="id5"><span>[G2015]</span></a>. This puts emphasis on the divergence of
individual conditional expectations from the mean line, thus making it easier
to explore heterogeneous relationships. cICE plots can be plotted by setting
<code class="docutils literal notranslate"><span class="pre">centered=True</span></code>:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">PartialDependenceDisplay</span><span class="o">.</span><span class="n">from_estimator</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">features</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">kind</span><span class="o">=</span><span class="s1">'both'</span><span class="p">,</span> <span class="n">centered</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="go"><...></span>
</pre></div>
</div>
</section>
<section id="mathematical-definition">
<h2><span class="section-number">4.1.3. </span>Mathematical Definition<a class="headerlink" href="#mathematical-definition" title="Link to this heading">#</a></h2>
<p>Let <span class="math notranslate nohighlight">\(X_S\)</span> be the set of input features of interest (i.e. the <code class="docutils literal notranslate"><span class="pre">features</span></code>
parameter) and let <span class="math notranslate nohighlight">\(X_C\)</span> be its complement.</p>
<p>The partial dependence of the response <span class="math notranslate nohighlight">\(f\)</span> at a point <span class="math notranslate nohighlight">\(x_S\)</span> is
defined as:</p>
<div class="math notranslate nohighlight">
\[\begin{split}pd_{X_S}(x_S) &\overset{def}{=} \mathbb{E}_{X_C}\left[ f(x_S, X_C) \right]\\
&= \int f(x_S, x_C) p(x_C) dx_C,\end{split}\]</div>
<p>where <span class="math notranslate nohighlight">\(f(x_S, x_C)\)</span> is the response function (<a class="reference internal" href="../glossary.html#term-predict"><span class="xref std std-term">predict</span></a>,
<a class="reference internal" href="../glossary.html#term-predict_proba"><span class="xref std std-term">predict_proba</span></a> or <a class="reference internal" href="../glossary.html#term-decision_function"><span class="xref std std-term">decision_function</span></a>) for a given sample whose
values are defined by <span class="math notranslate nohighlight">\(x_S\)</span> for the features in <span class="math notranslate nohighlight">\(X_S\)</span>, and by
<span class="math notranslate nohighlight">\(x_C\)</span> for the features in <span class="math notranslate nohighlight">\(X_C\)</span>. Note that <span class="math notranslate nohighlight">\(x_S\)</span> and
<span class="math notranslate nohighlight">\(x_C\)</span> may be tuples.</p>
<p>Computing this integral for various values of <span class="math notranslate nohighlight">\(x_S\)</span> produces a PDP plot
as above. An ICE line is defined as a single <span class="math notranslate nohighlight">\(f(x_{S}, x_{C}^{(i)})\)</span>
evaluated at <span class="math notranslate nohighlight">\(x_{S}\)</span>.</p>
</section>
<section id="computation-methods">
<h2><span class="section-number">4.1.4. </span>Computation methods<a class="headerlink" href="#computation-methods" title="Link to this heading">#</a></h2>
<p>There are two main methods to approximate the integral above, namely the
‘brute’ and ‘recursion’ methods. The <code class="docutils literal notranslate"><span class="pre">method</span></code> parameter controls which method
to use.</p>
<p>The ‘brute’ method is a generic method that works with any estimator. Note that
computing ICE plots is only supported with the ‘brute’ method. It
approximates the above integral by computing an average over the data <code class="docutils literal notranslate"><span class="pre">X</span></code>:</p>
<div class="math notranslate nohighlight">
\[pd_{X_S}(x_S) \approx \frac{1}{n_\text{samples}} \sum_{i=1}^n f(x_S, x_C^{(i)}),\]</div>
<p>where <span class="math notranslate nohighlight">\(x_C^{(i)}\)</span> is the value of the i-th sample for the features in
<span class="math notranslate nohighlight">\(X_C\)</span>. For each value of <span class="math notranslate nohighlight">\(x_S\)</span>, this method requires a full pass
over the dataset <code class="docutils literal notranslate"><span class="pre">X</span></code> which is computationally intensive.</p>
<p>Each of the <span class="math notranslate nohighlight">\(f(x_{S}, x_{C}^{(i)})\)</span> corresponds to one ICE line evaluated
at <span class="math notranslate nohighlight">\(x_{S}\)</span>. Computing this for multiple values of <span class="math notranslate nohighlight">\(x_{S}\)</span>, one
obtains a full ICE line. As one can see, the average of the ICE lines
correspond to the partial dependence line.</p>
<p>The ‘recursion’ method is faster than the ‘brute’ method, but it is only
supported for PDP plots by some tree-based estimators. It is computed as
follows. For a given point <span class="math notranslate nohighlight">\(x_S\)</span>, a weighted tree traversal is performed:
if a split node involves an input feature of interest, the corresponding left
or right branch is followed; otherwise both branches are followed, each branch
being weighted by the fraction of training samples that entered that branch.
Finally, the partial dependence is given by a weighted average of all the
visited leaves values.</p>
<p>With the ‘brute’ method, the parameter <code class="docutils literal notranslate"><span class="pre">X</span></code> is used both for generating the
grid of values <span class="math notranslate nohighlight">\(x_S\)</span> and the complement feature values <span class="math notranslate nohighlight">\(x_C\)</span>.
However with the ‘recursion’ method, <code class="docutils literal notranslate"><span class="pre">X</span></code> is only used for the grid values:
implicitly, the <span class="math notranslate nohighlight">\(x_C\)</span> values are those of the training data.</p>
<p>By default, the ‘recursion’ method is used for plotting PDPs on tree-based
estimators that support it, and ‘brute’ is used for the rest.</p>
<div class="admonition note" id="pdp-method-differences">
<p class="admonition-title">Note</p>
<p>While both methods should be close in general, they might differ in some
specific settings. The ‘brute’ method assumes the existence of the
data points <span class="math notranslate nohighlight">\((x_S, x_C^{(i)})\)</span>. When the features are correlated,
such artificial samples may have a very low probability mass. The ‘brute’
and ‘recursion’ methods will likely disagree regarding the value of the
partial dependence, because they will treat these unlikely
samples differently. Remember, however, that the primary assumption for
interpreting PDPs is that the features should be independent.</p>
</div>
<p class="rubric">Examples</p>
<ul class="simple">
<li><p><a class="reference internal" href="../auto_examples/inspection/plot_partial_dependence.html#sphx-glr-auto-examples-inspection-plot-partial-dependence-py"><span class="std std-ref">Partial Dependence and Individual Conditional Expectation Plots</span></a></p></li>
</ul>
<p class="rubric">Footnotes</p>
<aside class="footnote-list brackets">
<aside class="footnote brackets" id="id6" role="doc-footnote">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id1">1</a><span class="fn-bracket">]</span></span>
<p>For classification, the target response may be the probability of a
class (the positive class for binary classification), or the decision
function.</p>
</aside>
</aside>
<p class="rubric">References</p>
<div role="list" class="citation-list">
<div class="citation" id="h2009" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id2">H2009</a><span class="fn-bracket">]</span></span>
<p>T. Hastie, R. Tibshirani and J. Friedman,
<a class="reference external" href="https://web.stanford.edu/~hastie/ElemStatLearn//">The Elements of Statistical Learning</a>,
Second Edition, Section 10.13.2, Springer, 2009.</p>
</div>
<div class="citation" id="m2019" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span><a role="doc-backlink" href="#id4">M2019</a><span class="fn-bracket">]</span></span>
<p>C. Molnar,
<a class="reference external" href="https://christophm.github.io/interpretable-ml-book/">Interpretable Machine Learning</a>,
Section 5.1, 2019.</p>
</div>
<div class="citation" id="g2015" role="doc-biblioentry">
<span class="label"><span class="fn-bracket">[</span>G2015<span class="fn-bracket">]</span></span>
<span class="backrefs">(<a role="doc-backlink" href="#id3">1</a>,<a role="doc-backlink" href="#id5">2</a>)</span>
<p><a class="reference external" href="https://arxiv.org/abs/1309.6392">A. Goldstein, A. Kapelner, J. Bleich, and E. Pitkin,
“Peeking Inside the Black Box: Visualizing Statistical
Learning With Plots of Individual Conditional Expectation”
Journal of Computational and Graphical Statistics,
24(1): 44-65, Springer, 2015.</a></p>
</div>
</div>
</section>
</section>
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