Random Forest Classification Script Documentation

This documentation covers the usage of the script.py Python script, which performs classification using the Random Forest algorithm.

Overview

The script performs data preprocessing, feature selection, and classification on a given dataset. The goal is to train a model to classify data points effectively.

Dependencies

Before running the script, ensure that the following Python libraries are installed:

• pandas (for data manipulation)
• numpy (for numerical operations)
• sklearn (for machine learning tasks)
• graphviz (for visualizing decision trees, if needed)

Dataset

The script expects a CSV file named dataset.csv located in the same directory as the script.

Usage

Run the script using the following command:

python script.py

Functionalities

Data Preprocessing

The script reads the dataset and prepares it by dropping non-feature columns and separating the features and target variable.

Feature Selection

Feature selection is performed using a variance threshold to reduce dimensionality and remove low-variance features.

Model Training

The script splits the data into training and test sets, and it is assumed that a Random Forest classifier is then trained on the preprocessed and feature-selected data.

Model Evaluation

Performance metrics such as a confusion matrix and classification report are generated to evaluate the classifier's performance.

Expected Outputs

The script outputs the performance metrics of the trained classifier and may also output a visual representation of a decision tree (if graphviz is used).

Notes

The script is a template for Random Forest classification and may require adjustments based on the specifics of the dataset and classification task.