Decision Tree and Random Forest
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Decision-Tree-Random-Forest-Classification
A decision tree is a supervised machine learning algorithm that can be used for both classification and regression problems. It is simply a series of sequential decisions made to reach a specific result.
But often, a single tree is not sufficient for producing effective results. Random Forest is a tree-based machine learning algorithm that leverages the power of multiple decision trees for making decisions.
Dataset
The dataset consists of 400 rows and 3 features, including Age, EstimatedSalary, and Purchased. Here, the target variable is Purchased, which indicates whether a person purchased the item or not.
Dataset Sample
|Age|EstimatedSalary|Purchased| |—|—————|———| |19|19000|0| |35|20000|0| |32|150000|1|
Visualization and Accuracy
Decision Tree
Training Set
Test Set
Accuracy: 0.91
Random Forest
Training Set
Test Set
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