How would you approach model selection for a machine learning problem with limited labeled data?

In my final year project, I worked on a classification problem where we had limited labeled data for identifying plant species. To tackle this, I researched and implemented simple models like logistic regression and decision trees, which are effective with small datasets. I used techniques such as cross-validation to ensure that my model was not overfitting. Additionally, I explored data augmentation methods to artificially increase the dataset size by creating variations of the existing images. This approach helped improve the model's robustness, and I was able to achieve a decent accuracy rate with minimal data.

During my time volunteering for a community health initiative, we faced challenges collecting sufficient labeled data for predicting health outcomes. To address this, I collaborated with team members to gather additional unlabeled data from surveys. We then employed semi-supervised learning techniques, combining our small set of labeled data with the much larger unlabeled data. I also suggested using simpler models to start with, which allowed us to quickly iterate and refine our approach. This experience taught me the importance of resourcefulness and collaboration in model selection when data is limited.

In my first role as a data analyst, I encountered a project where we had limited labeled data to predict customer churn. I began by selecting simpler models like logistic regression that are less prone to overfitting with small data sizes. I also utilized techniques such as feature engineering to extract meaningful insights from the available data, and I integrated unsupervised learning methods to identify patterns in the customer behavior data. This combined approach not only enhanced model performance but also provided valuable insights to the marketing team for their strategies.