Ensemble Machine Learning Techniques 2.3: Ensemble Learning for Classification

The video tutorial covers the use of the Iris dataset to classify plant species using different voting techniques, including hard and soft voting. It begins with setting up the environment by importing necessary libraries and loading the dataset. The tutorial then explains the initialization and training of three classification models: decision tree, k-nearest neighbor, and support vector machine. It evaluates the accuracy of each model individually before combining them using hard and soft voting strategies. The video also demonstrates using the voting classifier from SK Learn and concludes with a summary and a preview of the next video on regression techniques.

Explain the concept of hard voting in the context of model predictions.

What is the significance of using soft voting compared to hard voting?