This GitHub repository explores the significance of bagging in the realm of machine learning. It showcases how bagging can notably enhance the performance of neural networks when tasked with solving complex multi-class classification problems, exemplified using the Iris dataset.
Bagging, short for "bootstrap aggregating," is a potent technique designed to combat overfitting. It achieves this by creating multiple data subsets, training individual models on each subset, and then combining their predictions.
The core focus of this project is to underscore how the bagging algorithm can greatly improve model performance and stability. We gauge the ensemble model's effectiveness using fundamental metrics like accuracy, precision, recall, F1-score, and log-loss, drawing comparisons with baseline models.
By exploring this repository, you'll gain insights into the pivotal role of bagging in machine learning. It offers a clear perspective on how bagging can elevate model performance, making it an invaluable tool for addressing complex classification challenges.
This code demonstrates the use of bagging with neural networks to improve classification performance. It trains a set of neural networks on an Iris dataset using different bagging strategies, evaluates their individual and combined accuracy and visualizes the loss curves and training accuracy, demonstrating the effectiveness of learning file
This program provides an interactive interface in which the user can select preferred method of generating sample bags, also known as bootstrap samples. There are methods for small bags, small bags without repeats, disjunctive distributions, and disjunctive bags . After the user selects an option, the program proceeds to generate bag samples using the selected method. If the resulting data does not match any of the available methods, the program will generate an error indicating an incorrect strategy bagging strategy. This interaction allows users to experiment with different methods of generating bag samples, providing an opportunity to observe how these methods can affect the performance of a later generated ensemble model.
To evaluate the algorithm, create separate visualizations for each approach for accuracy, logarithmic loss, recall, and F1-score. graphs for each metric, we can efficiently compare and contrast the differences in performance between strategies. In the bar charts created, each blue bar represents a performance metric (e.g., accuracy, logarithmic loss, recall, or F1 score) of an individual model in the set. The height of the bar indicates the value of the metric for that model. In the same graph, the red dashed line represents the same performance metric, but for the entire ensemble model, which is the joint result of the work of all individual models. The position of the red line relative to the blue bars visually visually illustrates how the performance of the ensemble model compares to the performance of the individual models.
