Decision Tree in Machine Learning

Hey, we will dive into decision trees in machine learning, it will be super fun like our family tree. Let’s get started,

What is a Decision Tree?

A Decision Tree is a non-parametric supervised learning algorithm for Classification and Regression Tasks (CART).

It has a hierarchical tree structure consisting of a root node, branches, an internal node, and a leaf node. Internal nodes represent a dataset, branches represent the decision rules and leaves represent the outcomes in this tree-structured technique.

Algorithm:

1. The root is the entire training set.
2. The values of the root attribute are compared with the dataset. Based on comparison, it follows the branch and jumps to the next node.
3. For the next node, it again compares the attribute value with the other sub-nodes and moves further.
4. Repeat step 3 until it reaches the leaf node.

Decision Tree

Types:

1. Decision Tree Classifier (Categorical target Variable)
2. Decision Tree regressor (Continuous target variable)

Implementation:

Here is the salary dataset,

Salary Dataset

Now, it is Implementation Time.

import pandas as pd
df=pd.read_csv("salaries.csv")
df.head()
inputs=df.drop("Salary_more_than_100k",axis="columns")
target=df["Salary_more_than_100k"]
from sklearn.preprocessing import LabelEncoder
le_Company=LabelEncoder()
le_Job=LabelEncoder()
le_Degree=LabelEncoder()
inputs["Company_n"]=le_Company.fit_transform(inputs["Company"])
inputs["Job_n"]=le_Job.fit_transform(inputs["Job"])
inputs["Degree_n"]=le_Degree.fit_transform(inputs["Degree"])
inputs.head()
inputs_n=inputs.drop(["Company","Job","Degree"],axis="columns")
inputs_n
from sklearn import tree
model=tree.DecisionTreeClassifier()
model.fit(inputs_n,target)
model.score(inputs_n,target)
model.predict([[2,2,1]])
model.predict([[2,0,1]])

Here you can access the full code:

Decision_Tree/Decision_Tree.ipynb at main · kaviya2478/Decision_Tree (github.com)

Thank you, Let’s drink some Chocolate Milk :)