5. A company serves a free-to-play online game with over a million active users. The game profits by inducing players to spend money on loot boxes. A Machine Learning Specialist uses data from 500,000 random users to train an XGBoost model that predicts players who are likely to buy at least 5 boxes within a month based on age, gender, playing hours, engagement patterns, etc. The collected data contains 150,000 positive samples and 350,000 negative samples. The model has high accuracy on the training dataset but low on the test dataset. The company wants to maximize profit by capturing as many players who are likely to buy as possible through targeted promotions. Which methods could the Specialist do to rectify the problem?Choose random samples of the training data and copy them to the test data.Tweak the cost function to give more weight to false negatives than false positives.Copy a subset of the positive samples and add noise to the copied data.Increase the maximum depth of a tree.Tweak the cost function to give more weight to false positives than false negatives
Question
- A company serves a free-to-play online game with over a million active users. The game profits by inducing players to spend money on loot boxes. A Machine Learning Specialist uses data from 500,000 random users to train an XGBoost model that predicts players who are likely to buy at least 5 boxes within a month based on age, gender, playing hours, engagement patterns, etc. The collected data contains 150,000 positive samples and 350,000 negative samples. The model has high accuracy on the training dataset but low on the test dataset. The company wants to maximize profit by capturing as many players who are likely to buy as possible through targeted promotions. Which methods could the Specialist do to rectify the problem?Choose random samples of the training data and copy them to the test data.Tweak the cost function to give more weight to false negatives than false positives.Copy a subset of the positive samples and add noise to the copied data.Increase the maximum depth of a tree.Tweak the cost function to give more weight to false positives than false negatives
Solution
The Specialist could consider the following methods to rectify the problem:
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Tweak the cost function to give more weight to false negatives than false positives: This method would help to reduce the number of false negatives, which in this case are the players who are likely to buy but are not identified by the model. By giving more weight to false negatives, the model would be more cautious and try to capture as many potential buyers as possible.
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Copy a subset of the positive samples and add noise to the copied data: This method is known as data augmentation. It can help to increase the diversity of the training data and reduce overfitting, which seems to be the problem here as the model performs well on the training data but poorly on the test data.
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Increase the maximum depth of a tree: This method could help to capture more complex patterns in the data, which might improve the model's performance on the test data. However, it should be done with caution as increasing the tree depth too much can lead to overfitting.
The other two options (copying random samples of the training data to the test data and giving more weight to false positives than false negatives) are not recommended. The first one would not help to improve the model's performance on unseen data, and the second one could lead to a high number of false positives, which would not be beneficial for the company's goal of maximizing profit.
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