Sklearn export_text

Sklearn export_text : Export the decision tree in text file

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Sklearn export_text is actually sklearn.tree.export package of sklearn.  Sklearn export_text gives an explainable view of the decision tree over a feature. In this article, We will firstly create a random decision tree and then we will export it, into text format.

Sklearn export_text: Step By step –

Step 1 (Prerequisites):  Decision Tree Creation –

Here we will create a random decision tree with the help of sci-kit learn library. We will use the iris dataset for decision tree creation.

from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import load_iris
iris = load_iris()
X = iris['data']
y = ['setosa']*50+['versicolor']*50+['virginica']*50
decision_tree = DecisionTreeClassifier(random_state=0, max_depth=3)
decision_tree = decision_tree.fit(X, y)
  1. Firstly, We need to import the DecisionTreeClassifier classifier from sklearn.tree module. For the dataset part, We need to invoke the load_iris() function.
  2. Secondly,  The X, Y are training and testing datasets.
  3. DecisionTreeClassifier is a constructer class for the decision tree. Here the max-depth argument is for defining the label of the decision tree.

Step 2:  Invoking sklearn export_text –

Once we have created the decision tree, We can export the decision tree into textual format. But to achieve this, We need to import export_text from sklearn.tree.export package. After it, We will invoke the export_text() function by passing the decision tree object as an argument. Here is the syntax for that.

from sklearn.tree.export import export_text
r = export_text(decision_tree, feature_names=iris['feature_names'],decimals=0, show_weights=True)
print(r)

we can easily solve the mystery of the decision tree with the above self-explanatory export_text() function. Here show_weights are set are True. It will give more info about each node. Let’s run the complete code together and check the output.

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As in the above tree text version, We can see the classes with weights. The discriminatory feature is also very clear in the text file.

Other Parameters of export_text() –

We have used decision_tree, feature_names, decimals, and  show_weights in the earlier section. We have max_depth and spacing as an optional parameter. Here the default value for max_depth is 10 and spacing is 3. decimals is also an optional parameter and its default value is 2.

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Thanks 

Data Science Learner Team

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