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<li class="toctree-l1 has-children"><a class="reference internal" href="../supervised_learning.html">1. Supervised learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="linear_model.html">1.1. Linear Models</a></li>
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<li class="toctree-l2"><a class="reference internal" href="neural_networks_supervised.html">1.17. Neural network models (supervised)</a></li>
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<li class="toctree-l2"><a class="reference internal" href="neural_networks_unsupervised.html">2.9. Neural network models (unsupervised)</a></li>
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<li class="toctree-l2"><a class="reference internal" href="cross_validation.html">3.1. Cross-validation: evaluating estimator performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="grid_search.html">3.2. Tuning the hyper-parameters of an estimator</a></li>
<li class="toctree-l2"><a class="reference internal" href="classification_threshold.html">3.3. Tuning the decision threshold for class prediction</a></li>
<li class="toctree-l2"><a class="reference internal" href="model_evaluation.html">3.4. Metrics and scoring: quantifying the quality of predictions</a></li>
<li class="toctree-l2 current active"><a class="current reference internal" href="#">3.5. Validation curves: plotting scores to evaluate models</a></li>
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<li class="toctree-l2"><a class="reference internal" href="partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
<li class="toctree-l2"><a class="reference internal" href="permutation_importance.html">4.2. Permutation feature importance</a></li>
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<li class="toctree-l2"><a class="reference internal" href="compose.html">6.1. Pipelines and composite estimators</a></li>
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<li class="toctree-l2"><a class="reference internal" href="unsupervised_reduction.html">6.5. Unsupervised dimensionality reduction</a></li>
<li class="toctree-l2"><a class="reference internal" href="random_projection.html">6.6. Random Projection</a></li>
<li class="toctree-l2"><a class="reference internal" href="kernel_approximation.html">6.7. Kernel Approximation</a></li>
<li class="toctree-l2"><a class="reference internal" href="metrics.html">6.8. Pairwise metrics, Affinities and Kernels</a></li>
<li class="toctree-l2"><a class="reference internal" href="preprocessing_targets.html">6.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="../datasets.html">7. Dataset loading utilities</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../datasets/toy_dataset.html">7.1. Toy datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="../datasets/real_world.html">7.2. Real world datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="../datasets/sample_generators.html">7.3. Generated datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="../datasets/loading_other_datasets.html">7.4. Loading other datasets</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="../computing.html">8. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../computing/scaling_strategies.html">8.1. Strategies to scale computationally: bigger data</a></li>
<li class="toctree-l2"><a class="reference internal" href="../computing/computational_performance.html">8.2. Computational Performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="../computing/parallelism.html">8.3. Parallelism, resource management, and configuration</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="../model_persistence.html">9. Model persistence</a></li>
<li class="toctree-l1"><a class="reference internal" href="../common_pitfalls.html">10. Common pitfalls and recommended practices</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../dispatching.html">11. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="array_api.html">11.1. Array API support (experimental)</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="../machine_learning_map.html">12. Choosing the right estimator</a></li>
<li class="toctree-l1"><a class="reference internal" href="../presentations.html">13. External Resources, Videos and Talks</a></li>
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<section id="validation-curves-plotting-scores-to-evaluate-models">
<span id="learning-curves"></span><h1><span class="section-number">3.5. </span>Validation curves: plotting scores to evaluate models<a class="headerlink" href="#validation-curves-plotting-scores-to-evaluate-models" title="Link to this heading">#</a></h1>
<p>Every estimator has its advantages and drawbacks. Its generalization error
can be decomposed in terms of bias, variance and noise. The <strong>bias</strong> of an
estimator is its average error for different training sets. The <strong>variance</strong>
of an estimator indicates how sensitive it is to varying training sets. Noise
is a property of the data.</p>
<p>In the following plot, we see a function <span class="math notranslate nohighlight">\(f(x) = \cos (\frac{3}{2} \pi x)\)</span>
and some noisy samples from that function. We use three different estimators
to fit the function: linear regression with polynomial features of degree 1,
4 and 15. We see that the first estimator can at best provide only a poor fit
to the samples and the true function because it is too simple (high bias),
the second estimator approximates it almost perfectly and the last estimator
approximates the training data perfectly but does not fit the true function
very well, i.e. it is very sensitive to varying training data (high variance).</p>
<figure class="align-center">
<a class="reference external image-reference" href="../auto_examples/model_selection/plot_underfitting_overfitting.html"><img alt="../_images/sphx_glr_plot_underfitting_overfitting_001.png" src="../_images/sphx_glr_plot_underfitting_overfitting_001.png" style="width: 700.0px; height: 250.0px;" />
</a>
</figure>
<p>Bias and variance are inherent properties of estimators and we usually have to
select learning algorithms and hyperparameters so that both bias and variance
are as low as possible (see <a class="reference external" href="https://en.wikipedia.org/wiki/Bias-variance_dilemma">Bias-variance dilemma</a>). Another way to reduce
the variance of a model is to use more training data. However, you should only
collect more training data if the true function is too complex to be
approximated by an estimator with a lower variance.</p>
<p>In the simple one-dimensional problem that we have seen in the example it is
easy to see whether the estimator suffers from bias or variance. However, in
high-dimensional spaces, models can become very difficult to visualize. For
this reason, it is often helpful to use the tools described below.</p>
<p class="rubric">Examples</p>
<ul class="simple">
<li><p><a class="reference internal" href="../auto_examples/model_selection/plot_underfitting_overfitting.html#sphx-glr-auto-examples-model-selection-plot-underfitting-overfitting-py"><span class="std std-ref">Underfitting vs. Overfitting</span></a></p></li>
<li><p><a class="reference internal" href="../auto_examples/model_selection/plot_train_error_vs_test_error.html#sphx-glr-auto-examples-model-selection-plot-train-error-vs-test-error-py"><span class="std std-ref">Effect of model regularization on training and test error</span></a></p></li>
<li><p><a class="reference internal" href="../auto_examples/model_selection/plot_learning_curve.html#sphx-glr-auto-examples-model-selection-plot-learning-curve-py"><span class="std std-ref">Plotting Learning Curves and Checking Models’ Scalability</span></a></p></li>
</ul>
<section id="validation-curve">
<span id="id1"></span><h2><span class="section-number">3.5.1. </span>Validation curve<a class="headerlink" href="#validation-curve" title="Link to this heading">#</a></h2>
<p>To validate a model we need a scoring function (see <a class="reference internal" href="model_evaluation.html#model-evaluation"><span class="std std-ref">Metrics and scoring: quantifying the quality of predictions</span></a>),
for example accuracy for classifiers. The proper way of choosing multiple
hyperparameters of an estimator is of course grid search or similar methods
(see <a class="reference internal" href="grid_search.html#grid-search"><span class="std std-ref">Tuning the hyper-parameters of an estimator</span></a>) that select the hyperparameter with the maximum score
on a validation set or multiple validation sets. Note that if we optimize
the hyperparameters based on a validation score the validation score is biased
and not a good estimate of the generalization any longer. To get a proper
estimate of the generalization we have to compute the score on another test
set.</p>
<p>However, it is sometimes helpful to plot the influence of a single
hyperparameter on the training score and the validation score to find out
whether the estimator is overfitting or underfitting for some hyperparameter
values.</p>
<p>The function <a class="reference internal" href="generated/sklearn.model_selection.validation_curve.html#sklearn.model_selection.validation_curve" title="sklearn.model_selection.validation_curve"><code class="xref py py-func docutils literal notranslate"><span class="pre">validation_curve</span></code></a> can help in this case:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">validation_curve</span>
<span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <span class="n">load_iris</span>
<span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.svm</span><span class="w"> </span><span class="kn">import</span> <span class="n">SVC</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</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">load_iris</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">indices</span><span class="p">)</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">X</span><span class="p">[</span><span class="n">indices</span><span class="p">],</span> <span class="n">y</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">train_scores</span><span class="p">,</span> <span class="n">valid_scores</span> <span class="o">=</span> <span class="n">validation_curve</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s2">"linear"</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="n">param_name</span><span class="o">=</span><span class="s2">"C"</span><span class="p">,</span> <span class="n">param_range</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">7</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span>
<span class="gp">... </span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">train_scores</span>
<span class="go">array([[0.90..., 0.94..., 0.91..., 0.89..., 0.92...],</span>
<span class="go"> [0.9... , 0.92..., 0.93..., 0.92..., 0.93...],</span>
<span class="go"> [0.97..., 1... , 0.98..., 0.97..., 0.99...]])</span>
<span class="gp">>>> </span><span class="n">valid_scores</span>
<span class="go">array([[0.9..., 0.9... , 0.9... , 0.96..., 0.9... ],</span>
<span class="go"> [0.9..., 0.83..., 0.96..., 0.96..., 0.93...],</span>
<span class="go"> [1.... , 0.93..., 1.... , 1.... , 0.9... ]])</span>
</pre></div>
</div>
<p>If you intend to plot the validation curves only, the class
<a class="reference internal" href="generated/sklearn.model_selection.ValidationCurveDisplay.html#sklearn.model_selection.ValidationCurveDisplay" title="sklearn.model_selection.ValidationCurveDisplay"><code class="xref py py-class docutils literal notranslate"><span class="pre">ValidationCurveDisplay</span></code></a> is more direct than
using matplotlib manually on the results of a call to <a class="reference internal" href="generated/sklearn.model_selection.validation_curve.html#sklearn.model_selection.validation_curve" title="sklearn.model_selection.validation_curve"><code class="xref py py-func docutils literal notranslate"><span class="pre">validation_curve</span></code></a>.
You can use the method
<a class="reference internal" href="generated/sklearn.model_selection.ValidationCurveDisplay.html#sklearn.model_selection.ValidationCurveDisplay.from_estimator" title="sklearn.model_selection.ValidationCurveDisplay.from_estimator"><code class="xref py py-meth docutils literal notranslate"><span class="pre">from_estimator</span></code></a> similarly
to <a class="reference internal" href="generated/sklearn.model_selection.validation_curve.html#sklearn.model_selection.validation_curve" title="sklearn.model_selection.validation_curve"><code class="xref py py-func docutils literal notranslate"><span class="pre">validation_curve</span></code></a> to generate and plot the validation curve:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <span class="n">load_iris</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">ValidationCurveDisplay</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.svm</span><span class="w"> </span><span class="kn">import</span> <span class="n">SVC</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.utils</span><span class="w"> </span><span class="kn">import</span> <span class="n">shuffle</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">load_iris</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">shuffle</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="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">ValidationCurveDisplay</span><span class="o">.</span><span class="n">from_estimator</span><span class="p">(</span>
<span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s2">"linear"</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="n">param_name</span><span class="o">=</span><span class="s2">"C"</span><span class="p">,</span> <span class="n">param_range</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">7</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="p">)</span>
</pre></div>
</div>
<figure class="align-center">
<img alt="../_images/learning_curve-1.png" class="plot-directive" src="../_images/learning_curve-1.png" />
</figure>
<p>If the training score and the validation score are both low, the estimator will
be underfitting. If the training score is high and the validation score is low,
the estimator is overfitting and otherwise it is working very well. A low
training score and a high validation score is usually not possible.</p>
</section>
<section id="learning-curve">
<span id="id2"></span><h2><span class="section-number">3.5.2. </span>Learning curve<a class="headerlink" href="#learning-curve" title="Link to this heading">#</a></h2>
<p>A learning curve shows the validation and training score of an estimator
for varying numbers of training samples. It is a tool to find out how much
we benefit from adding more training data and whether the estimator suffers
more from a variance error or a bias error. Consider the following example
where we plot the learning curve of a naive Bayes classifier and an SVM.</p>
<p>For the naive Bayes, both the validation score and the training score
converge to a value that is quite low with increasing size of the training
set. Thus, we will probably not benefit much from more training data.</p>
<p>In contrast, for small amounts of data, the training score of the SVM is
much greater than the validation score. Adding more training samples will
most likely increase generalization.</p>
<figure class="align-center">
<a class="reference external image-reference" href="../auto_examples/model_selection/plot_learning_curve.html"><img alt="../_images/sphx_glr_plot_learning_curve_001.png" src="../_images/sphx_glr_plot_learning_curve_001.png" style="width: 500.0px; height: 300.0px;" />
</a>
</figure>
<p>We can use the function <a class="reference internal" href="generated/sklearn.model_selection.learning_curve.html#sklearn.model_selection.learning_curve" title="sklearn.model_selection.learning_curve"><code class="xref py py-func docutils literal notranslate"><span class="pre">learning_curve</span></code></a> to generate the values
that are required to plot such a learning curve (number of samples
that have been used, the average scores on the training sets and the
average scores on the validation sets):</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">learning_curve</span>
<span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.svm</span><span class="w"> </span><span class="kn">import</span> <span class="n">SVC</span>
<span class="gp">>>> </span><span class="n">train_sizes</span><span class="p">,</span> <span class="n">train_scores</span><span class="p">,</span> <span class="n">valid_scores</span> <span class="o">=</span> <span class="n">learning_curve</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s1">'linear'</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="n">train_sizes</span><span class="o">=</span><span class="p">[</span><span class="mi">50</span><span class="p">,</span> <span class="mi">80</span><span class="p">,</span> <span class="mi">110</span><span class="p">],</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">train_sizes</span>
<span class="go">array([ 50, 80, 110])</span>
<span class="gp">>>> </span><span class="n">train_scores</span>
<span class="go">array([[0.98..., 0.98 , 0.98..., 0.98..., 0.98...],</span>
<span class="go"> [0.98..., 1. , 0.98..., 0.98..., 0.98...],</span>
<span class="go"> [0.98..., 1. , 0.98..., 0.98..., 0.99...]])</span>
<span class="gp">>>> </span><span class="n">valid_scores</span>
<span class="go">array([[1. , 0.93..., 1. , 1. , 0.96...],</span>
<span class="go"> [1. , 0.96..., 1. , 1. , 0.96...],</span>
<span class="go"> [1. , 0.96..., 1. , 1. , 0.96...]])</span>
</pre></div>
</div>
<p>If you intend to plot the learning curves only, the class
<a class="reference internal" href="generated/sklearn.model_selection.LearningCurveDisplay.html#sklearn.model_selection.LearningCurveDisplay" title="sklearn.model_selection.LearningCurveDisplay"><code class="xref py py-class docutils literal notranslate"><span class="pre">LearningCurveDisplay</span></code></a> will be easier to use.
You can use the method
<a class="reference internal" href="generated/sklearn.model_selection.LearningCurveDisplay.html#sklearn.model_selection.LearningCurveDisplay.from_estimator" title="sklearn.model_selection.LearningCurveDisplay.from_estimator"><code class="xref py py-meth docutils literal notranslate"><span class="pre">from_estimator</span></code></a> similarly
to <a class="reference internal" href="generated/sklearn.model_selection.learning_curve.html#sklearn.model_selection.learning_curve" title="sklearn.model_selection.learning_curve"><code class="xref py py-func docutils literal notranslate"><span class="pre">learning_curve</span></code></a> to generate and plot the learning curve:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <span class="n">load_iris</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">LearningCurveDisplay</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.svm</span><span class="w"> </span><span class="kn">import</span> <span class="n">SVC</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.utils</span><span class="w"> </span><span class="kn">import</span> <span class="n">shuffle</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">load_iris</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">shuffle</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="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">LearningCurveDisplay</span><span class="o">.</span><span class="n">from_estimator</span><span class="p">(</span>
<span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s2">"linear"</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="n">train_sizes</span><span class="o">=</span><span class="p">[</span><span class="mi">50</span><span class="p">,</span> <span class="mi">80</span><span class="p">,</span> <span class="mi">110</span><span class="p">],</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
</pre></div>
</div>
<figure class="align-center">
<img alt="../_images/learning_curve-2.png" class="plot-directive" src="../_images/learning_curve-2.png" />
</figure>
<p class="rubric">Examples</p>
<ul class="simple">
<li><p>See <a class="reference internal" href="../auto_examples/model_selection/plot_learning_curve.html#sphx-glr-auto-examples-model-selection-plot-learning-curve-py"><span class="std std-ref">Plotting Learning Curves and Checking Models’ Scalability</span></a> for an
example of using learning curves to check the scalability of a predictive model.</p></li>
</ul>
</section>
</section>
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