Random Forest Classifier App
The "Random Forest Classifier App" is a web-based application designed for interactive data input and visualization of feature importance. It provides an intuitive interface for users to define, input, and analyze datasets, leveraging a simulated Random Forest Classifier.
Features:
○ Users can customize the dataset structure by specifying the number of rows (5-50) and columns (5-8).
○ Table headers can be edited to define feature names, while individual cells capture the dataset values.
○ Generates random predictions (e.g., "Class A" or "Class B") for the provided dataset.
○ Calculates feature importance values for each column, simulating the output of a Random Forest Classifier.
○ A bar chart displays the feature importance scores, making it easy to identify the most significant features.
Applications
○ Educational tool for teaching Random Forest concepts.
○ Prototyping or experimenting with feature importance for small datasets.
Click on the picture to go to the app.
This tool combines simplicity and functionality, making it ideal for introductory machine learning experiments or presentations on data science workflows.