Discover the reasons behind Watson AutoAI’s choice of Binary Classification as the Prediction Type for risk assessment, and learn how this decision impacts model performance and accuracy.
Table of Contents
Question
Why did Watson AutoAI select Binary Classification as your Prediction Type?
A. Because predicting risk or no risk has only two options
B. Because you really only needed two models to make your point
C. Because you were dividing your training data into two portions
D. Because you only needed to manage Accuracy and Run time
Answer
A. Because predicting risk or no risk has only two options
Explanation
Your client wanted an AI model that would predict either risk or no risk. That is a binary prediction, with only two options.
Watson AutoAI selected Binary Classification as the Prediction Type because the problem at hand involves predicting a binary outcome: whether there is a risk or no risk. Binary Classification is used when the target variable has only two possible classes or categories.
In this case, the model is tasked with predicting the presence or absence of risk based on the provided training data. Since there are only two possible outcomes (risk or no risk), Binary Classification is the most appropriate choice for the Prediction Type.
The other options are incorrect for the following reasons:
B. The number of models needed to make a point is not a factor in determining the Prediction Type. The choice is based on the nature of the problem and the target variable.
C. Dividing the training data into two portions (e.g., training and validation sets) is a common practice in machine learning, but it does not dictate the Prediction Type. The Prediction Type is determined by the number of classes in the target variable.
D. Managing Accuracy and Run time are important considerations in model development, but they do not directly influence the selection of the Prediction Type. The Prediction Type is chosen based on the characteristics of the target variable and the problem at hand.
In summary, Watson AutoAI selected Binary Classification as the Prediction Type because the problem involves predicting a binary outcome (risk or no risk), making it the most suitable choice for this particular task.
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