Week 7: Model Training and Experimentation

Hi everyone, welcome back! The main goal of this week was to scale up the modeling process by training more models and initiating more structured experimentation. With the pipeline now fully connected, the goal was to explore how different modeling approaches and feature configurations impact model performance.

A major part of this week was training and comparing multiple models. In addition to the baseline models from last week, I started testing more sophisticated models like Support Vector Machines (SVM), XGBoost, and Neural Networks. Each of these models captures different types of patterns in the data, so comparing their performance provides insight into which modeling strategies are the most effective and which are not. Running these models within the pipeline also helped make sure that the system can be scaled to a range of architectures without any significant modifications.

I also spent time experimenting with different feature sets and transcript embedding strategies. Since the data includes both structured survey responses and transcript data, the way this information is represented matters a lot. I researched variations in which features are included, as well as different ways in which the transcript embeddings might be incorporated into the model. Even small changes here can have a major effect on the results, so this part was more about testing and observing what actually makes a difference.

Along with model and feature experimentation, I started doing hyperparameter tuning within the pipeline. Instead of adjusting parameters manually, the pipeline is programmed to test and try various settings in a more systematic way. This allows the analysis of a combination of parameters in a more controlled and reproducible manner. By integrating hyperparameter tuning directly into the workflow, the system can efficiently search for better-performing model configurations.

Overall, this week was devoted to the transition from the initial model training to more sophisticated experimentation. Combining multiple models, different feature choices, and parameter tuning gave a better sense of how these decisions impact model performance. With this experimentation framework in place, the next step is to analyze the results more deeply and figure out which approaches are worth dedicating more time to improving further.

Next week, the model will focus on evaluating model performance and comparing results more systematically. See you then!