This Jupyter Notebook contains code for implementing ensemble learning techniques on the Labeled Faces in the Wild (LFW) dataset to improve facial recognition accuracy. The LFW dataset contains facial images of various individuals captured under different conditions, and the notebook is structured in a way that first introduces the concept of ensemble learning.
- Introduction
- Ensemble Learning
- Data Preparation
- Models and Training
- Bagging and Boosting
- Results and Discussion
The LFW dataset is a popular benchmark for face recognition algorithms, and the goal of this project is to use ensemble learning techniques to improve the accuracy of facial recognition on this dataset. The notebook begins with an overview of the LFW dataset, including its contents and structure.
The notebook introduces the concept of ensemble learning, which involves combining the predictions of multiple models to achieve better performance than any of the individual models. The notebook presents two ensemble learning methods, namely bagging and boosting, which are implemented using the Scikit-learn library.
The notebook provides code for loading and preprocessing the LFW dataset, including dividing it into a training set and a test set, and further dividing the training set into five subsets to train the individual models that will be used in the ensemble.
The notebook implements two models, a Support Vector Machine (SVM) and a Random Forest classifier, which are trained on each of the five subsets of the training data. The notebook explains the use of these models and their hyperparameters and provides code to train the models.
Once the individual models are trained, the notebook proceeds to implement bagging and boosting. Bagging involves training multiple models independently and averaging their predictions, while boosting involves training models sequentially and adjusting their weights based on their performance. The notebook provides code to implement both bagging and boosting using the Scikit-learn library.
The notebook evaluates the performance of the ensemble methods on the test data, reporting the accuracy achieved by each method. The results indicate that both bagging and boosting improve the accuracy of facial recognition compared to using a single model. The notebook also provides a discussion of the results, including the advantages and limitations of using ensemble learning on the LFW dataset, as well as suggestions for further research and experimentation.
Overall, this Jupyter Notebook provides a useful and informative resource for learning about ensemble learning techniques and their application to the LFW dataset. The code is well-documented and easy to follow, making it a good starting point for those interested in exploring ensemble learning further.