Random Forest (RF)

Train each tree independently, using a random sample of the data.

Pros:

• Reduced risk of overfitting

• Provides flexibility

• Easy to determine feature importance Cons:

• Time-consuming process

• Requires more resources

• More complex https://www.ibm.com/cloud/learn/random-forest

• RF uses random feature selection, which lowers correlation and therefore the variance of the regression trees

• How to build a Random Forest?

  1. Bootstraped new Datasets for RF
  • create new dataset from original one with same nr of entries
  • entries are picked randomly
  • Random Sampling with Replacement → choose entries for new DS randomly
  2. Randomly Select features for each new DS and train new DT
  3. A new data point gets passed through each DT and note the predictions
  4. All predictions are combined, and through majority voting the prediction is made
  • The process of combining results from multiple models is called Aggregation.

• Bagging = Bootstrapping + Aggregation

  • Bootstrapping with not the entirety of the features makes our RF less sensitive to training data

• What is the ideal size of the feature subset used for training our DTs?

  • Research shows that a nr close to the log or sqrt of the total number of features is good https://www.youtube.com/watch?v=v6VJ2RO66Ag

Taken from https://corporatefinanceinstitute.com/resources/data-science/random-forest/

Q: What is the difference between Classification and Regression?

• Both are supervised machine learning
• Classification works with a categorical response variable
• Regression works with a continuous response variable

Q: What model to use for a rating prediction (1-5 Stars)?

• Ratings are ordinal numbers and maintain a natural ordering. Therefore we can use a regression Model.
• If we discard the ordering, we can also use a classification model.
• Source: 1 to 5 Star Ratings — Classification or Regression? | by Sebastian Poliak | Towards Data Science
• This paper suggests that Maximum Entropy Classification models performs better than regression models for rating systems

  Maximum Entropy Text classification means: start with least informative weights (priors) and optimize to find weights that maximize the likelihood of the data, the P(D). Essentially, it’s the EM algorithm.

  • Stack Overflow, https://stackoverflow.com/questions/37228196/what-is-maximum-entropy

Q: How can RF be used for a regression model? A: When combining the predictions of our DTs (aggregation), we can take the mean over all predictions for get a continuous number. We are basically transforming our categorical prediction to a regression one.

Q: What is Model Pruning? A: Model pruning is the art of discarding those weights that do not signify a model’s performance. https://towardsdatascience.com/scooping-into-model-pruning-in-deep-learning-da92217b84ac

RandomForestRegressor in Scikit-Learn can be used https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html

Using Random Forest for Rating Prediction

https://www.researchgate.net/publication/309775673_A_random_forest_approach_for_rating-based_recommender_system ”… movies and the values are centered on zero by subtracting the mean from the respective elements.”

• should we center our values around zero too by substracting the mean?
• centered age with its average