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<h1 id="scikit-garden">Scikit-garden</h1>
<p>Scikit-garden or skgarden (pronounced as skarden) is a garden for scikit-learn compatible trees.</p>
<h2 id="installation">Installation</h2>
<p>Scikit-Garden depends on NumPy, SciPy, Scikit-Learn and Cython. So make sure these dependencies are installed using pip:</p>
<pre><code>pip install setuptools numpy scipy scikit-learn cython
</code></pre>
<p>After that Scikit-Garden can be installed using pip.</p>
<pre><code>pip install scikit-garden
</code></pre>
<h2 id="usage">Usage</h2>
<p>The estimators in Scikit-Garden are Scikit-Learn compatible and can serve as a drop-in replacement for Scikit-Learn's trees and forests.</p>
<pre><code class="python">from sklearn.datasets import load_boston
boston = load_boston()
X, y = boston.data, boston.target
### Use MondrianForests for variance estimation
from skgarden import MondrianForestRegressor
mfr = MondrianForestRegressor()
mfr.fit(X, y)
y_mean, y_std = mfr.predict(X, return_std=True)
### Use QuantileForests for quantile estimation
from skgarden import RandomForestQuantileRegressor
rfqr = RandomForestQuantileRegressor(random_state=0)
rfqr.fit(X, y)
y_mean = rfqr.predict(X)
y_median = rfqr.predict(X, 50)
</code></pre>
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