Decision Trees u0026 Random Forests for Game Behavior Prediction Quiz Quiz

Explore essential concepts of decision trees and random forests for predicting player behavior in games. This quiz helps reinforce your understanding of game behavior modeling, feature selection, and evaluating predictive models for interactive environments.

1. Purpose of Decision Trees

   In the context of predicting player strategies in a strategic board game, what is the main advantage of using decision trees?

   1. They require a constant number of features for every player.
   2. They always use ensemble learning methods.
   3. They guarantee perfect prediction accuracy.
   4. They can automatically select the most relevant player actions for prediction.

   Explanation: Decision trees excel at identifying and selecting the most relevant features for splitting decisions, such as key player actions in game behavior prediction. While constant feature numbers (Option B) are not required, decision trees can handle varying data. They do not guarantee perfect accuracy (Option C), and although they can be used in ensembles, standalone decision trees don't always use them (Option D).

2. Overfitting in Decision Trees

   When analyzing player behavior in an online game, why might a single large decision tree result in overfitting?

   1. Because it ignores important player statistics.
   2. Because it always uses the same feature at each split.
   3. Because it creates overly complex rules that match rare behaviors in the training data.
   4. Because it can never categorize new players.

   Explanation: A single large decision tree can memorize and match rare or coincidental patterns, leading to overfitting. This means the model may not generalize well to new, unseen player actions. Option B is incorrect because decision trees can categorize new players, although performance might decay with overfitting. Option C isn't accurate; decision trees don't inherently ignore features. Option D is also wrong since decision trees evaluate all features at each node.

3. Random Forest Advantages

   How do random forests improve the prediction of outcomes in a multiplayer racing game compared to individual decision trees?

   1. By removing randomness from the model training process.
   2. By using only one tree for all player data.
   3. By combining multiple diverse trees to reduce prediction variance.
   4. By always producing the simplest possible model.

   Explanation: Random forests build many decision trees on random subsets of data and features, then aggregate their outputs, reducing variance and improving generalization. Option B contradicts the ensemble nature of forests, while Option C is incorrect since randomness is a feature, not a flaw. Option D misunderstands the concept; forests do not necessarily create the simplest models.

4. Feature Importance Evaluation

   Which method can be used to determine which player characteristics most influence game win predictions in a random forest model?

   1. Measuring feature importance scores.
   2. Counting the leaves in each tree.
   3. Using only the first feature in the dataset.
   4. Sorting the feature names alphabetically.

   Explanation: Feature importance scores indicate how much each variable, like player level or number of actions, contributes to prediction accuracy in a random forest. Counting leaves (Option B) doesn't reveal feature relevance. Sorting names alphabetically (Option C) is unrelated to prediction influence. Using the first feature only (Option D) ignores the usefulness of other factors.

5. Evaluating Predictive Performance

   Which metric is most appropriate for assessing how well a random forest predicts if a player will finish a platform game level based on gameplay data?

   1. Number of features used
   2. Run time complexity
   3. Alphabetical order of players
   4. Accuracy

   Explanation: Accuracy measures how often the model correctly predicts player outcomes, making it the most appropriate evaluation metric for a classification task like game completion. Run time complexity (Option B) addresses efficiency, not predictive performance. Player alphabetical order (Option C) and the number of features used (Option D) are unrelated to assessing prediction success.