Discover why IBM’s Watson tested four distinct algorithms when creating an AI model. Learn about the role of algorithm comparison in optimizing model performance and accuracy.
Table of Contents
Question
Why did Watson test four different algorithms for this AI model?
A. To remove the least effective model from the data set
B. To compare four different versions of the data set for accuracy
C. To determine which set of algorithms predicted defaults most effectively
D. To identify those algorithms that triggered the confusion matrix
Answer
Watson tested four different algorithms for this AI model in order to determine which set of algorithms predicted defaults most effectively (Option C).
Explanation
The AutoAI in IBM Watson Studio can test up to four different algorithms in an experiment to determine which algorithm and adjustments can produce the most accurate predictions on the test data.
When developing an AI model, it’s important to evaluate multiple algorithms to identify the one that performs best for the specific task at hand. Different algorithms have different strengths and weaknesses. Some may be better at handling certain types of data or patterns, while others may be more prone to certain errors or biases.
By testing four algorithms, Watson aimed to empirically determine which approach yielded the most accurate predictions of loan defaults. This comparative analysis allows the AI developers to select the top-performing algorithm(s) to use in the final model.
The other options are incorrect:
A) Removing the least effective model from the dataset would not require testing four algorithms. The algorithms are being compared, not the data.
B) Comparing dataset versions for accuracy would involve varying the training data, not the algorithms.
D) A confusion matrix is used to evaluate a model’s performance after selecting an algorithm, not as a criteria for picking algorithms.
In summary, testing multiple algorithms through controlled experiments is a key step in optimizing an AI model’s architecture. It allows developers to gather objective performance data to inform their decision of which algorithm(s) to ultimately deploy in production, with the goal of maximizing the model’s predictive accuracy.
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