Interactive Visualization of Decision Trees

## https://towardsdatascience.com/interactive-visualization-of-decision-trees-with-jupyter-widgets-ca15dd312084
## Interactive Visualization of Decision Trees with Jupyter Widgets
from sklearn.tree import DecisionTreeClassifier, export_graphviz
from sklearn import tree
from sklearn.datasets import load_wine
from IPython.display import SVG
from graphviz import Source
from IPython.display import display
# load dataset
data = load_wine()

# feature matrix
X = data.data

# target vector
y = data.target

# class labels
labels = data.feature_names

# print dataset description
print(data.DESCR)
estimator = DecisionTreeClassifier()
estimator.fit(X, y)

graph = Source(tree.export_graphviz(estimator, out_file=None
   , feature_names=labels, class_names=['0', '1', '2'] 
   , filled = True))
display(SVG(graph.pipe(format='svg')))



pip install ipywidgets
jupyter nbextension enable --py widgetsnbextension


conda install -c conda-forge ipywidgets



from sklearn.tree import DecisionTreeClassifier, export_graphviz
from sklearn import tree
from sklearn.datasets import load_wine
from IPython.display import SVG
from graphviz import Source
from IPython.display import display                               
from ipywidgets import interactive
# load dataset
data = load_wine()
# feature matrix
X = data.data
# target vector
y = data.target
# class labels
labels = data.feature_names
def plot_tree(crit, split, depth, min_split, min_leaf=0.2):
estimator = DecisionTreeClassifier(random_state = 0 
      , criterion = crit
      , splitter = split
      , max_depth = depth
      , min_samples_split=min_split
      , min_samples_leaf=min_leaf)
    estimator.fit(X, y)
graph = Source(tree.export_graphviz(estimator
      , out_file=None
      , feature_names=labels
      , class_names=['0', '1', '2']
      , filled = True))
   
    display(SVG(graph.pipe(format='svg')))
return estimator
inter=interactive(plot_tree 
   , crit = ["gini", "entropy"]
   , split = ["best", "random"]
   , depth=[1,2,3,4]
   , min_split=(0.1,1)
   , min_leaf=(0.1,0.5))
display(inter)