Wanelo_rf.7z Page

The model generates a ranked list of product IDs predicted to have the highest probability of being saved by that user. 4. Evaluation

Use Precision@K and Recall@K to evaluate how many of the top-K recommended products were actually relevant to the user [2, 3]. To help you develop this further, could you tell me: Wanelo_RF.7z

What is in the (e.g., user-save data, product metadata)? The model generates a ranked list of product

Tune hyperparameters (e.g., n_estimators , max_depth ) for accuracy [2]. 3. Feature Integration (API Implementation) To help you develop this further, could you

Train a RandomForestClassifier on user-product interaction features to predict future interaction.

Create vectors for users based on categories saved, price points, and interaction frequency.

Based on the filename "Wanelo_RF.7z," this appears to be an archive containing data related to (a former social shopping platform) likely for a Random Forest (RF) machine learning model .