100k Rf Facebook.xlsx «Limited Time»

If your interest is in the algorithm itself applied to this scale:

Papers in this category often use datasets of 100K+ users to predict psychological traits or engagement.

: Optimizing Facebook ad campaigns using Random Forest for ROI prediction. 100K RF FACEBOOK.xlsx

: Predicting personality or "Likes" using ensemble methods.

: Researchers frequently use Random Forest models to analyze large-scale CSV/XLSX exports of Facebook data to predict user attributes like age, gender, or political leaning. If your interest is in the algorithm itself

: 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.

: Many datasets labeled "100K" are used to train classifiers (like RF) to detect spam or misinformation on Facebook. Key Source : Detecting Fake News on Social Media (ACM) . 4. Technical Specification: Random Forest (RF) : Researchers frequently use Random Forest models to

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