Multi-Class Model for Kaggle Tabular Playground Series February 2022 Using Decision Forests

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 Kaggle Tabular Playground Series February 2022 dataset is a multi-class modeling situation where we are trying to predict one of several (more than two) possible outcomes.

INTRODUCTION: Kaggle wants to provide an approachable environment for relatively new people in their data science journey. Since January 2021, they have hosted playground-style competitions on Kaggle with fun but less complex, tabular datasets. For this dataset, we want to predict bacteria species based on repeated lossy measurements of DNA snippets. Each row of data contains a spectrum of histograms generated by repeated measurements of a sample, and each row contains the output of all 286 histogram possibilities.

ANALYSIS: The performance of the preliminary Gradient Boosted Trees model achieved an accuracy benchmark of 99.36% on the training dataset. When we applied the finalized model to Kaggle’s test dataset, the model achieved an accuracy score of 91.55%.

CONCLUSION: In this iteration, the TensorFlow Decision Forests model appeared to be a suitable algorithm for modeling this dataset.

Dataset Used: Kaggle Tabular Playground Series February 2022

Dataset ML Model: Multi-Class classification with numerical attributes

Dataset Reference:

One potential source of performance benchmark:

The HTML formatted report can be found here on GitHub.