8000 DOC Minor updates to the Decision Tree User Guide by justmarkham · Pull Request #16905 · scikit-learn/scikit-learn · GitHub
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DOC Minor updates to the Decision Tree User Guide #16905

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Merged
merged 5 commits into from
Apr 14, 2020
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DOC Minor updates to the Decision Tree User Guide #16905

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de8f9f9
DOC Tree pruning is now supported
justmarkham Apr 12, 2020
0e2af3b
DOC link to the plot_tree function
justmarkham Apr 12, 2020
dd75b37
DOC Simplify example since clf is already fitted
justmarkham Apr 12, 2020
55367e8
DOC Fix formatting
justmarkham Apr 12, 2020
665d4f5
DOC Fix example to import from non-deprecated module
justmarkham Apr 12, 2020
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17 changes: 7 additions & 10 deletions doc/modules/tree.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -56,9 +56,9 @@ The disadvantages of decision trees include:

- Decision-tree learners can create over-complex trees that do not
generalise the data well. This is called overfitting. Mechanisms
such as pruning (not currently supported), setting the minimum
number of samples required at a leaf node or setting the maximum
depth of the tree are necessary to avoid this problem.
such as pruning, setting the minimum number of samples required
at a leaf node or setting the maximum depth of the tree are
necessary to avoid this problem.

- Decision trees can be unstable because small variations in the
data might result in a completely different tree being generated.
Expand Down Expand Up @@ -124,10 +124,10 @@ Using the Iris dataset, we can construct a tree as follows::
>>> clf = tree.DecisionTreeClassifier()
>>> clf = clf.fit(X, y)

Once trained, you can plot the tree with the plot_tree function::
Once trained, you can plot the tree with the :func:`plot_tree` function::


>>> tree.plot_tree(clf.fit(iris.data, iris.target)) # doctest: +SKIP
>>> tree.plot_tree(clf) # doctest: +SKIP

.. figure:: ../auto_examples/tree/images/sphx_glr_plot_iris_dtc_002.png
:target: ../auto_examples/tree/plot_iris_dtc.html
Expand All @@ -137,10 +137,7 @@ Once trained, you can plot the tree with the plot_tree function::
We can also export the tree in `Graphviz
<https://www.graphviz.org/>`_ format using the :func:`export_graphviz`
exporter. If you use the `conda <https://conda.io>`_ package manager, the graphviz binaries

and the python package can be installed with

conda install python-graphviz
and the python package can be installed with `conda install python-graphviz`.
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Not sure if this change is necessary, but LGTM anyway.


Alternatively binaries for graphviz can be downloaded from the graphviz project homepage,
and the Python wrapper installed from pypi with `pip install graphviz`.
Expand Down Expand Up @@ -188,7 +185,7 @@ of external libraries and is more compact:

>>> from sklearn.datasets import load_iris
>>> from sklearn.tree import DecisionTreeClassifier
>>> from sklearn.tree.export import export_text
>>> from sklearn.tree import export_text
>>> iris = load_iris()
>>> decision_tree = DecisionTreeClassifier(random_state=0, max_depth=2)
>>> decision_tree = decision_tree.fit(iris.data, iris.target)
Expand Down
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