Decision Trees and KNN
This project focuses on detecting real and fake news articles using supervised machine learning algorithms. The goal is to classify news articles as either real or fake by training models on labeled datasets. We explore different machine learning algorithms, including Decision Trees and K-Nearest Neighbors (KNN), to evaluate their performance in detecting fake news.
š³ 1. Decision Trees Decision Trees are a type of supervised learning algorithm that is used for classification and regression. It builds a tree-like model of decisions based on input features and their values. The tree splits the data into subsets based on the most significant feature at each step until it reaches a classification decision.
Advantages:
Easy to interpret and visualize. Handles both numerical and categorical data. Requires little data preprocessing. Disadvantages:
Prone to overfitting without proper pruning. Sensitive to noisy data. š 2. K-Nearest Neighbors (KNN) K-Nearest Neighbors (KNN) is a non-parametric classification algorithm that classifies a data point based on the labels of its nearest neighbors. It works by calculating the distance between the data point and its neighbors, assigning the most common label among the closest neighbors.
Advantages:
Simple and easy to implement. No training phaseāinstance-based learning. Performs well for small datasets. Disadvantages:
Computationally expensive for large datasets. Sensitive to the choice of k and feature scaling.