Decision-tree-classifier/decision-tree-classifier.py at main · PyMachine-Collective/Decision-tree-classifier · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
# Decision Tree Classifier Implementation using Sklearn
# Step1: Load the data
from sklearn import datasets
iris = datasets.load_iris()
X = iris.data
y = iris.target

# Step2: Split the data
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2,
random_state = 42)

# Step3: train the model
from sklearn import tree
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X_train,y_train)
pred = clf.predict(X_test)

# Step4: evaluate the model performance using test set
from sklearn.metrics import accuracy_score
acc = accuracy_score(y_test,pred)
print('accuracy rate', acc)
# accuracy rate 1.0

# Step5: print the decision tree
import graphviz
dot_data = tree.export_graphviz(clf, out_file=None,
                         feature_names=iris.feature_names,
                         class_names=iris.target_names,
                         filled=True, rounded=True,
                         special_characters=True)
graph = graphviz.Source(dot_data)
graph.render("iris classification")
# 'iris classification.pdf'

import graphviz
dot_data = tree.export_graphviz(clf, out_file=None,
                         feature_names=iris.feature_names,
                         class_names=iris.target_names,
                         filled=True, rounded=True,
                         special_characters=True)
graph = graphviz.Source(dot_data)
graph