Visualize Decision Tree

Please select your Decision Tree and visualize it. Visualization works for classifier and regressor trees. Powered by Super Tree, developed in house by MLJAR

decision-treesupertree

Required packages

You need below packages to use the code generated by recipe. All packages are automatically installed in MLJAR Studio.

supertree>=0.0.4

Interactive recipe

You can use below interactive recipe to generate code. This recipe is available in MLJAR Studio.

In the below recipe, we assume that you have following variables available in your notebook:

my_classifier (type DecisionTreeClassifier)
my_regressor (type DecisionTreeRegressor)
X (type DataFrame)
y (type Series)

Python code

# Python code will be here

Code explanation

Creates matplotlib figure with Decision Tree visualization.

Scikit-learn cookbook

Code recipes from Scikit-learn cookbook.

Decision Tree
Random Forest
k-Nearest Neighbors
Train Model
Compute Predictions
Visualize Decision Tree
Hyper Parameters Search
Feature Importance
Compute Metric
Confusion Matrix
ROC Curve
Precision-Recall
Lift Chart
Calibration PLot
k-Means

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