What factors would you consider when choosing a model for a classification problem?

During my final year project, I worked on an image classification task where we had to identify different species of plants from photos. I considered several factors when choosing our model. First, I assessed the size and quality of our dataset; since we had a relatively small dataset, I opted for a simpler model like logistic regression instead of a complex deep learning model. Additionally, I focused on interpretability since our project aimed to provide insights for botany students. After trying out a few models, I found logistic regression gave us a good balance of accuracy and simplicity, allowing us to explain our results easily to our classmates.

While volunteering for a local community event, I was tasked with analyzing participant feedback to classify responses as positive, negative, or neutral. I considered the volume of feedback we received and the need for quick insights. Given the urgency, I chose a Naive Bayes classifier, as it is efficient for text classification and can handle large datasets well. I also wanted a model that could be easily explained to the event organizers, so I prioritized interpretability. The model performed well and helped us make timely decisions to improve future events based on participant sentiment.

In my first job as a data analyst, I worked on a project to segment customers based on purchasing behavior. Given the diverse data types we had, I took into account factors such as the dimensionality of the data and the need for interpretability. I considered using decision trees because they are easy to visualize and understand. However, after testing, I realized that a random forest model provided better accuracy and handled overfitting concerns. I presented both options to my team, highlighting their pros and cons, which helped us choose the best model for our needs.