Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.
SUMMARY: The project aims to construct a predictive model using various machine learning algorithms and document the end-to-end steps using a template. The Avila Bible Identification dataset is a multi-class modeling situation where we attempt to predict one of several (more than two) possible outcomes.
INTRODUCTION: The Avila dataset includes 800 images extracted from the “Avila Bible,” a giant Latin copy of the whole Bible produced during the XII century between Italy and Spain. The paleographic analysis of the manuscript has identified the presence of 12 transcribers; however, each transcriber did not transcribe the same number of pages. The prediction task is to associate each pattern to one of the 12 transcribers labeled as A, B, C, D, E, F, G, H, I, W, X, and Y. The research team normalized the data using the Z-normalization method and divided the dataset into two portions, training and test. The training set contains 10,430 samples, while the test set contains 10,437 samples.
ANALYSIS: The average performance of the machine learning algorithms achieved an accuracy benchmark of 85.51% using the training dataset. Furthermore, we selected Bagging Classifier as the final model as it processed the training dataset with a final accuracy score of 98.53%. When we processed the test dataset with the final model, the model achieved an accuracy score of 99.20%.
CONCLUSION: In this iteration, the Bagging Classifier model appeared to be a suitable algorithm for modeling this dataset.
Dataset Used: Avila Bible Dataset
Dataset ML Model: Multi-Class classification with numerical features
Dataset Reference: https://archive-beta.ics.uci.edu/ml/datasets/avila
One source of potential performance benchmarks: https://www.sciencedirect.com/science/article/abs/pii/S0952197618300721
The HTML formatted report can be found here on GitHub.