: Random Forest is preferred for 100K-row datasets because it handles high-dimensional data (many columns in an .xlsx) without the extensive preprocessing required by deep learning.
: Unlike "black box" deep learning, RF allows for "feature importance" analysis, showing exactly which Facebook metrics (e.g., shares vs. comments) are the strongest predictors. 100K RF FACEBOOK.xlsx
: Predicting personality or "Likes" using ensemble methods. : Random Forest is preferred for 100K-row datasets
Knowing the origin will help in finding the specific "deep paper" or documentation you need. : Predicting personality or "Likes" using ensemble methods
While the exact "deep paper" for that specific .xlsx file isn't publicly indexed, the following research areas represent the most likely "deep" academic context for such a dataset: 1. Facebook User Behavior & Prediction
: A "100K" dataset might contain performance metrics for 100,000 ad sets. The "RF" would refer to the Random Forest model used to determine which factors (bid price, creative, frequency) lead to the best conversion. 3. Fake News & Bot Detection