Robust Ensemble Learning

Recipes and ratings play a pivotal role in our everyday lives, influencing various aspects from culinary experiences to social interactions. Predicting users’ preferences is particularly crucial as it enables personalized experiences and enhances efficiency in content discovery. In our analysis, we focus on how to predict user preferences based on various numerical and textual features. Our approach involves employing advanced techniques such as TF-IDF transformation, PCA, and a homogenous ensemble learning method, specifically Random Forest, to construct a reliable multi-class classifier with more robust and reliable predictions even facing imbalanced datasets, ensuring dependable predictions in scenarios where data distribution is skewed.

Figure credit from Encord