What factors would you consider when selecting a model for a given machine learning problem?

In my final year project, I worked on predicting house prices using a dataset from my university. I considered several factors when selecting my model. First, I looked at the data type; since it included both numerical and categorical variables, I opted for a decision tree model due to its ability to handle mixed data types effectively. I also considered the interpretability of the model, as my project required me to explain my findings to a non-technical audience. I aimed for a balance between accuracy and simplicity, which led me to choose a decision tree over a more complex ensemble method. The results were promising, and I received positive feedback for my clear communication of the model's insights.

During a volunteer stint with a local non-profit, I helped analyze social media sentiment regarding their campaigns. To select the model, I first gathered insights on the dataset, which consisted of text data. I initially thought of using a simple logistic regression due to its straightforward implementation. However, I also considered the need for better accuracy and the ability to capture nuances in language. I decided on using a Naive Bayes classifier, which performed well for text classification tasks. This experience taught me the importance of aligning the model choice with the problem's requirements while still being mindful of the resources available to us as a small organization.

In my first job as a data analyst, I was tasked with predicting customer churn for a subscription service. I evaluated various models, including logistic regression and random forests. I realized that while random forests could provide higher accuracy, they required more computational resources and were harder to interpret for stakeholders. I ultimately chose logistic regression for its simplicity and ease of explanation, which was crucial for our business meetings. This experience reinforced my understanding of balancing model performance with interpretability and resource constraints, a lesson I carry with me in my career.