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  <section id="getting-started">
<h1>Getting Started<a class="headerlink" href="#getting-started" title="Link to this heading">#</a></h1>
<p>The purpose of this guide is to illustrate some of the main features that
<code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> provides. It assumes a very basic working knowledge of
machine learning practices (model fitting, predicting, cross-validation,
etc.). Please refer to our <a class="reference internal" href="install.html#installation-instructions"><span class="std std-ref">installation instructions</span></a> for installing <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code>.</p>
<p><code class="docutils literal notranslate"><span class="pre">Scikit-learn</span></code> is an open source machine learning library that supports
supervised and unsupervised learning. It also provides various tools for
model fitting, data preprocessing, model selection, model evaluation,
and many other utilities.</p>
<section id="fitting-and-predicting-estimator-basics">
<h2>Fitting and predicting: estimator basics<a class="headerlink" href="#fitting-and-predicting-estimator-basics" title="Link to this heading">#</a></h2>
<p><code class="docutils literal notranslate"><span class="pre">Scikit-learn</span></code> provides dozens of built-in machine learning algorithms and
models, called <a class="reference internal" href="glossary.html#term-estimators"><span class="xref std std-term">estimators</span></a>. Each estimator can be fitted to some data
using its <a class="reference internal" href="glossary.html#term-fit"><span class="xref std std-term">fit</span></a> method.</p>
<p>Here is a simple example where we fit a
<a class="reference internal" href="modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble.RandomForestClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">RandomForestClassifier</span></code></a> to some very basic data:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span> <span class="o">=</span> <span class="n">RandomForestClassifier</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">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span> <span class="mi">1</span><span class="p">,</span>  <span class="mi">2</span><span class="p">,</span>  <span class="mi">3</span><span class="p">],</span>  <span class="c1"># 2 samples, 3 features</span>
<span class="gp">... </span>     <span class="p">[</span><span class="mi">11</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">13</span><span class="p">]]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">y</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="c1"># classes of each sample</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">clf</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="go">RandomForestClassifier(random_state=0)</span>
</pre></div>
</div>
<p>The <a class="reference internal" href="glossary.html#term-fit"><span class="xref std std-term">fit</span></a> method generally accepts 2 inputs:</p>
<ul class="simple">
<li><p>The samples matrix (or design matrix) <a class="reference internal" href="glossary.html#term-X"><span class="xref std std-term">X</span></a>. The size of <code class="docutils literal notranslate"><span class="pre">X</span></code>
is typically <code class="docutils literal notranslate"><span class="pre">(n_samples,</span> <span class="pre">n_features)</span></code>, which means that samples are
represented as rows and features are represented as columns.</p></li>
<li><p>The target values <a class="reference internal" href="glossary.html#term-y"><span class="xref std std-term">y</span></a> which are real numbers for regression tasks, or
integers for classification (or any other discrete set of values). For
unsupervised learning tasks, <code class="docutils literal notranslate"><span class="pre">y</span></code> does not need to be specified. <code class="docutils literal notranslate"><span class="pre">y</span></code> is
usually a 1d array where the <code class="docutils literal notranslate"><span class="pre">i</span></code> th entry corresponds to the target of the
<code class="docutils literal notranslate"><span class="pre">i</span></code> th sample (row) of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p></li>
</ul>
<p>Both <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> are usually expected to be numpy arrays or equivalent
<a class="reference internal" href="glossary.html#term-array-like"><span class="xref std std-term">array-like</span></a> data types, though some estimators work with other
formats such as sparse matrices.</p>
<p>Once the estimator is fitted, it can be used for predicting target values of
new data. You don’t need to re-train the estimator:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>  <span class="c1"># predict classes of the training data</span>
<span class="go">array([0, 1])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">([[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">],</span> <span class="p">[</span><span class="mi">14</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</span> <span class="mi">16</span><span class="p">]])</span>  <span class="c1"># predict classes of new data</span>
<span class="go">array([0, 1])</span>
</pre></div>
</div>
<p>You can check <a class="reference internal" href="machine_learning_map.html#ml-map"><span class="std std-ref">Choosing the right estimator</span></a> on how to choose the right model for your use case.</p>
</section>
<section id="transformers-and-pre-processors">
<h2>Transformers and pre-processors<a class="headerlink" href="#transformers-and-pre-processors" title="Link to this heading">#</a></h2>
<p>Machine learning workflows are often composed of different parts. A typical
pipeline consists of a pre-processing step that transforms or imputes the
data, and a final predictor that predicts target values.</p>
<p>In <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code>, pre-processors and transformers follow the same API as
the estimator objects (they actually all inherit from the same
<code class="docutils literal notranslate"><span class="pre">BaseEstimator</span></code> class). The transformer objects don’t have a
<a class="reference internal" href="glossary.html#term-predict"><span class="xref std std-term">predict</span></a> method but rather a <a class="reference internal" href="glossary.html#term-transform"><span class="xref std std-term">transform</span></a> method that outputs a
newly transformed sample matrix <code class="docutils literal notranslate"><span class="pre">X</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">15</span><span class="p">],</span>
<span class="gp">... </span>     <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">10</span><span class="p">]]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># scale data according to computed scaling values</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">StandardScaler</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="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="go">array([[-1.,  1.],</span>
<span class="go">       [ 1., -1.]])</span>
</pre></div>
</div>
<p>Sometimes, you want to apply different transformations to different features:
the <a class="reference internal" href="modules/compose.html#column-transformer"><span class="std std-ref">ColumnTransformer</span></a> is designed for these
use-cases.</p>
</section>
<section id="pipelines-chaining-pre-processors-and-estimators">
<h2>Pipelines: chaining pre-processors and estimators<a class="headerlink" href="#pipelines-chaining-pre-processors-and-estimators" title="Link to this heading">#</a></h2>
<p>Transformers and estimators (predictors) can be combined together into a
single unifying object: a <a class="reference internal" href="modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>. The pipeline
offers the same API as a regular estimator: it can be fitted and used for
prediction with <code class="docutils literal notranslate"><span class="pre">fit</span></code> and <code class="docutils literal notranslate"><span class="pre">predict</span></code>. As we will see later, using a
pipeline will also prevent you from data leakage, i.e. disclosing some
testing data in your training data.</p>
<p>In the following example, we <a class="reference internal" href="datasets.html#datasets"><span class="std std-ref">load the Iris dataset</span></a>, split it
into train and test sets, and compute the accuracy score of a pipeline on
the test data:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">make_pipeline</span>
<span class="gp">&gt;&gt;&gt; </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">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">accuracy_score</span>
<span class="gp">...</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># create a pipeline object</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">pipe</span> <span class="o">=</span> <span class="n">make_pipeline</span><span class="p">(</span>
<span class="gp">... </span>    <span class="n">StandardScaler</span><span class="p">(),</span>
<span class="gp">... </span>    <span class="n">LogisticRegression</span><span class="p">()</span>
<span class="gp">... </span><span class="p">)</span>
<span class="gp">...</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># load the iris dataset and split it into train and test sets</span>
<span class="gp">&gt;&gt;&gt; </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">&gt;&gt;&gt; </span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</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="gp">...</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># fit the whole pipeline</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">pipe</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="go">Pipeline(steps=[(&#39;standardscaler&#39;, StandardScaler()),</span>
<span class="go">                (&#39;logisticregression&#39;, LogisticRegression())])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># we can now use it like any other estimator</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">pipe</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">),</span> <span class="n">y_test</span><span class="p">)</span>
<span class="go">0.97...</span>
</pre></div>
</div>
</section>
<section id="model-evaluation">
<h2>Model evaluation<a class="headerlink" href="#model-evaluation" title="Link to this heading">#</a></h2>
<p>Fitting a model to some data does not entail that it will predict well on
unseen data. This needs to be directly evaluated. We have just seen the
<a class="reference internal" href="modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split"><code class="xref py py-func docutils literal notranslate"><span class="pre">train_test_split</span></code></a> helper that splits a
dataset into train and test sets, but <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> provides many other
tools for model evaluation, in particular for <a class="reference internal" href="modules/cross_validation.html#cross-validation"><span class="std std-ref">cross-validation</span></a>.</p>
<p>We here briefly show how to perform a 5-fold cross-validation procedure,
using the <a class="reference internal" href="modules/generated/sklearn.model_selection.cross_validate.html#sklearn.model_selection.cross_validate" title="sklearn.model_selection.cross_validate"><code class="xref py py-func docutils literal notranslate"><span class="pre">cross_validate</span></code></a> helper. Note that
it is also possible to manually iterate over the folds, use different
data splitting strategies, and use custom scoring functions. Please refer to
our <a class="reference internal" href="modules/cross_validation.html#cross-validation"><span class="std std-ref">User Guide</span></a> for more details:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_regression</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LinearRegression</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">cross_validate</span>
<span class="gp">...</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_regression</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">1000</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">&gt;&gt;&gt; </span><span class="n">lr</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span>
<span class="gp">...</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">result</span> <span class="o">=</span> <span class="n">cross_validate</span><span class="p">(</span><span class="n">lr</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="c1"># defaults to 5-fold CV</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">result</span><span class="p">[</span><span class="s1">&#39;test_score&#39;</span><span class="p">]</span>  <span class="c1"># r_squared score is high because dataset is easy</span>
<span class="go">array([1., 1., 1., 1., 1.])</span>
</pre></div>
</div>
</section>
<section id="automatic-parameter-searches">
<h2>Automatic parameter searches<a class="headerlink" href="#automatic-parameter-searches" title="Link to this heading">#</a></h2>
<p>All estimators have parameters (often called hyper-parameters in the
literature) that can be tuned. The generalization power of an estimator
often critically depends on a few parameters. For example a
<a class="reference internal" href="modules/generated/sklearn.ensemble.RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor" title="sklearn.ensemble.RandomForestRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">RandomForestRegressor</span></code></a> has a <code class="docutils literal notranslate"><span class="pre">n_estimators</span></code>
parameter that determines the number of trees in the forest, and a
<code class="docutils literal notranslate"><span class="pre">max_depth</span></code> parameter that determines the maximum depth of each tree.
Quite often, it is not clear what the exact values of these parameters
should be since they depend on the data at hand.</p>
<p><code class="docutils literal notranslate"><span class="pre">Scikit-learn</span></code> provides tools to automatically find the best parameter
combinations (via cross-validation). In the following example, we randomly
search over the parameter space of a random forest with a
<a class="reference internal" href="modules/generated/sklearn.model_selection.RandomizedSearchCV.html#sklearn.model_selection.RandomizedSearchCV" title="sklearn.model_selection.RandomizedSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">RandomizedSearchCV</span></code></a> object. When the search
is over, the <a class="reference internal" href="modules/generated/sklearn.model_selection.RandomizedSearchCV.html#sklearn.model_selection.RandomizedSearchCV" title="sklearn.model_selection.RandomizedSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">RandomizedSearchCV</span></code></a> behaves as
a <a class="reference internal" href="modules/generated/sklearn.ensemble.RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor" title="sklearn.ensemble.RandomForestRegressor"><code class="xref py py-class docutils literal notranslate"><span class="pre">RandomForestRegressor</span></code></a> that has been fitted with
the best set of parameters. Read more in the <a class="reference internal" href="modules/grid_search.html#grid-search"><span class="std std-ref">User Guide</span></a>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_california_housing</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestRegressor</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">RandomizedSearchCV</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">randint</span>
<span class="gp">...</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">fetch_california_housing</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">&gt;&gt;&gt; </span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</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="gp">...</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># define the parameter space that will be searched over</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">param_distributions</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;n_estimators&#39;</span><span class="p">:</span> <span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span>
<span class="gp">... </span>                       <span class="s1">&#39;max_depth&#39;</span><span class="p">:</span> <span class="n">randint</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">)}</span>
<span class="gp">...</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># now create a searchCV object and fit it to the data</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">search</span> <span class="o">=</span> <span class="n">RandomizedSearchCV</span><span class="p">(</span><span class="n">estimator</span><span class="o">=</span><span class="n">RandomForestRegressor</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">n_iter</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
<span class="gp">... </span>                            <span class="n">param_distributions</span><span class="o">=</span><span class="n">param_distributions</span><span class="p">,</span>
<span class="gp">... </span>                            <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">search</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="go">RandomizedSearchCV(estimator=RandomForestRegressor(random_state=0), n_iter=5,</span>
<span class="go">                   param_distributions={&#39;max_depth&#39;: ...,</span>
<span class="go">                                        &#39;n_estimators&#39;: ...},</span>
<span class="go">                   random_state=0)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">search</span><span class="o">.</span><span class="n">best_params_</span>
<span class="go">{&#39;max_depth&#39;: 9, &#39;n_estimators&#39;: 4}</span>

<span class="gp">&gt;&gt;&gt; </span><span class="c1"># the search object now acts like a normal random forest estimator</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># with max_depth=9 and n_estimators=4</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">search</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
<span class="go">0.73...</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>In practice, you almost always want to <a class="reference internal" href="modules/grid_search.html#composite-grid-search"><span class="std std-ref">search over a pipeline</span></a>, instead of a single estimator. One of the main
reasons is that if you apply a pre-processing step to the whole dataset
without using a pipeline, and then perform any kind of cross-validation,
you would be breaking the fundamental assumption of independence between
training and testing data. Indeed, since you pre-processed the data
using the whole dataset, some information about the test sets are
available to the train sets. This will lead to over-estimating the
generalization power of the estimator (you can read more in this <a class="reference external" href="https://www.kaggle.com/alexisbcook/data-leakage">Kaggle
post</a>).</p>
<p>Using a pipeline for cross-validation and searching will largely keep
you from this common pitfall.</p>
</div>
</section>
<section id="next-steps">
<h2>Next steps<a class="headerlink" href="#next-steps" title="Link to this heading">#</a></h2>
<p>We have briefly covered estimator fitting and predicting, pre-processing
steps, pipelines, cross-validation tools and automatic hyper-parameter
searches. This guide should give you an overview of some of the main
features of the library, but there is much more to <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code>!</p>
<p>Please refer to our <a class="reference internal" href="user_guide.html#user-guide"><span class="std std-ref">User Guide</span></a> for details on all the tools that we
provide. You can also find an exhaustive list of the public API in the
<a class="reference internal" href="api/index.html#api-ref"><span class="std std-ref">API Reference</span></a>.</p>
<p>You can also look at our numerous <a class="reference internal" href="auto_examples/index.html#general-examples"><span class="std std-ref">examples</span></a> that
illustrate the use of <code class="docutils literal notranslate"><span class="pre">scikit-learn</span></code> in many different contexts.</p>
</section>
</section>


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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#fitting-and-predicting-estimator-basics">Fitting and predicting: estimator basics</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#transformers-and-pre-processors">Transformers and pre-processors</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pipelines-chaining-pre-processors-and-estimators">Pipelines: chaining pre-processors and estimators</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#model-evaluation">Model evaluation</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#automatic-parameter-searches">Automatic parameter searches</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#next-steps">Next steps</a></li>
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