My code is follow the class of machine learning of google.The two code are same.I don\'t know why it show error.May be the type of variable is error.But google\'s code is sa
I hope this helps, I was having a similar issue. I decided not to use pydot / pydotplus, but rather graphviz. I modified (barely) the code and it works wonders! :)
# 2. Train classifier
# Testing Data
# Examples used to "test" the classifier's accuracy
# Not part of the training data
import numpy as np
from sklearn.datasets import load_iris
from sklearn import tree
iris = load_iris()
test_idx = [0, 50, 100] # Grabs one example of each flower for testing data (in the data set it so happens to be that
# each flower begins at 0, 50, and 100
# training data
train_target = np.delete(iris.target, test_idx) # Delete all but 3 for training target data
train_data = np.delete(iris.data, test_idx, axis=0) # Delete all but 3 for training data
# testing data
test_target = iris.target[test_idx] # Get testing target data
test_data = iris.data[test_idx] # Get testing data
# create decision tree classifier and train in it on the testing data
clf = tree.DecisionTreeClassifier()
clf.fit(train_data, train_target)
# Predict label for new flower
print(test_target)
print(clf.predict(test_data))
# Visualize the tree
from sklearn.externals.six import StringIO
import graphviz
dot_data = StringIO()
tree.export_graphviz(clf,
out_file=dot_data,
feature_names=iris.feature_names,
class_names=iris.target_names,
filled=True, rounded=True,
impurity=False)
graph = graphviz.Source(dot_data.getvalue())
graph.render("iris.pdf", view=True)