draw a tree id3 python

Python Program to Implement Decision Tree ID3 Algorithm

Exp. No. 3. Write a program to demonstrate the working of the decision tree based ID3 algorithm. Use an appropriate data set for building the decision tree and apply this knowledge to classify a new sample.

Decision Tree ID3 Algorithm Machine Learning

ID3(Examples, Target_attribute, Attributes)  Examples are the training examples.  Target_attribute is the attribute whose value is to be predicted by the tree.  Attributes is a list of other attributes that may be tested by the learned decision tree.  Returns a decision tree that correctly classifies the given Examples.  Create a Root node for the tree If all Examples are positive, Return the single-node tree Root, with label = + If all Examples are negative, Return the single-node tree Root, with label = - If Attributes is empty, Return the single-node tree Root,  with label = most common value of Target_attribute in Examples  Otherwise Begin    A ← the attribute from Attributes that best* classifies Examples    The decision attribute for Root ← A    For each possible value, vi, of A,       Add a new tree branch below Root, corresponding to the test A = vi       Let Examples vi, be the subset of Examples that have value vi for A       If Examples vi , is empty          Then below this new branch add a leaf node with           label = most common value of Target_attribute in Examples       Else           below this new branch add the subtree           ID3(Examples vi, Targe_tattribute, Attributes – {A})) End Return Root              

The best attribute is the one with highest information gain

ENTROPY:

Entropy measures the impurity of a collection of examples.

Where, p₊ is the proportion of positive examples in S

p_– is the proportion of negative examples in S.

INFORMATION GAIN:

Information gain, is the expected reduction in entropy caused by partitioning the examples according to this attribute.

The information gain, Gain(S, A) of an attribute A, relative to a collection of examples S, is defined as

Dataset:

PlayTennis Dataset is saved as .csv (comma separated values) file in the current working directory otherwise use the complete path of the dataset set in the program:

Day	Outlook	Temperature	Humidity	Wind	PlayTennis
D1	Sunny	Hot	High	Weak	No
D2	Sunny	Hot	High	Strong	No
D3	Overcast	Hot	High	Weak	Yes
D4	Rain	Mild	High	Weak	Yes
D5	Rain	Cool	Normal	Weak	Yes
D6	Rain	Cool	Normal	Strong	No
D7	Overcast	Cool	Normal	Strong	Yes
D8	Sunny	Mild	High	Weak	No
D9	Sunny	Cool	Normal	Weak	Yes
D10	Rain	Mild	Normal	Weak	Yes
D11	Sunny	Mild	Normal	Strong	Yes
D12	Overcast	Mild	High	Strong	Yes
D13	Overcast	Hot	Normal	Weak	Yes
D14	Rain	Mild	High	Strong	No

Click here to download dataset

Python Program to Implement and Demonstrate FIND-S Algorithm

import pandas as pd import math import numpy as np  data = pd.read_csv("3-dataset.csv") features = [feat for feat in data] features.remove("answer")  class Node:     def __init__(self):         self.children = []         self.value = ""         self.isLeaf = False         self.pred = ""  def entropy(examples):     pos = 0.0     neg = 0.0     for _, row in examples.iterrows():         if row["answer"] == "yes":             pos += 1         else:             neg += 1     if pos == 0.0 or neg == 0.0:         return 0.0     else:         p = pos / (pos + neg)         n = neg / (pos + neg)         return -(p * math.log(p, 2) + n * math.log(n, 2))  def info_gain(examples, attr):     uniq = np.unique(examples[attr])     #print ("\n",uniq)     gain = entropy(examples)     #print ("\n",gain)     for u in uniq:         subdata = examples[examples[attr] == u]         #print ("\n",subdata)         sub_e = entropy(subdata)         gain -= (float(len(subdata)) / float(len(examples))) * sub_e         #print ("\n",gain)     return gain  def ID3(examples, attrs):     root = Node()      max_gain = 0     max_feat = ""     for feature in attrs:         #print ("\n",examples)         gain = info_gain(examples, feature)         if gain > max_gain:             max_gain = gain             max_feat = feature     root.value = max_feat     #print ("\nMax feature attr",max_feat)     uniq = np.unique(examples[max_feat])     #print ("\n",uniq)     for u in uniq:         #print ("\n",u)         subdata = examples[examples[max_feat] == u]         #print ("\n",subdata)         if entropy(subdata) == 0.0:             newNode = Node()             newNode.isLeaf = True             newNode.value = u             newNode.pred = np.unique(subdata["answer"])             root.children.append(newNode)         else:             dummyNode = Node()             dummyNode.value = u             new_attrs = attrs.copy()             new_attrs.remove(max_feat)             child = ID3(subdata, new_attrs)             dummyNode.children.append(child)             root.children.append(dummyNode)     return root  def printTree(root: Node, depth=0):     for i in range(depth):         print("\t", end="")     print(root.value, end="")     if root.isLeaf:         print(" -> ", root.pred)     print()     for child in root.children:         printTree(child, depth + 1)  root = ID3(data, features) printTree(root)

Output:

Solved Numerical Examples and Tutorial on Decision Trees Machine Learning:

1. How to build a decision Tree for Boolean Function Machine Learning

2. How to build a decision Tree for Boolean Function Machine Learning

3. How to build Decision Tree using ID3 Algorithm – Solved Numerical Example – 1

4. How to build Decision Tree using ID3 Algorithm – Solved Numerical Example -2

5. How to build Decision Tree using ID3 Algorithm – Solved Numerical Example -3

6. Appropriate Problems for Decision Tree Learning Machine Learning Big Data Analytics

7. How to find the Entropy and Information Gain in Decision Tree Learning

8. Issues in Decision Tree Learning Machine Learning

9. How to Avoid Overfitting in Decision Tree Learning, Machine Learning, and Data Mining

10. How to handle Continuous Valued Attributes in Decision Tree Learning, Machine Learning

Summary

This tutorial discusses how to Implement and demonstrate the Decision Tree ID3 Algorithm in Python. The training data is read from a .CSV file. If you like the tutorial share it with your friends. Like the Facebook page for regular updates and YouTube channel for video tutorials.

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