Learn how to design a machine learning model for predicting dog health based on age and body fat levels. Discover the right combination of features and labels to optimize accuracy and improve canine health predictions.
Table of Contents
Question
When creating a machine learning model for predicting the health of a dog based on its age and body fat levels, how many features and labels should you use?
A. One label and one feature
B. Two labels and one feature
C. Three labels and one feature
D. Two features and one label
Answer
D. Two features and one label
Explanation
This scenario needs:
- Two features: Age and body fat levels, as these are relevant factors influencing the dog’s health.
- One label: Representing the predicted health status, such as healthy, unhealthy, or a risk score.
This scenario involves multiple linear regression which extends simple linear regression to model the relationship between multiple features and a single continuous variable. It has a similar mathematical formulation to simple linear regression but includes additional features. It treats features as independent which is a crucial assumption for the model to function correctly.
Two labels and one feature would not be appropriate for predicting health. A single label representing the predicted health status (e.g., healthy, unhealthy) is required rather than two separate labels (e.g., sick, not sick).
One label and one feature would not be appropriate for predicting health. Using only one feature (e.g., age) would likely be insufficient for accurately predicting health as other factors such as breed, diet, and exercise also play a role.
Three labels and one feature would not be appropriate for predicting health. Similar to two labels, having three labels (e.g., healthy, unhealthy, unknown) would not be ideal for a prediction. A single label representing the predicted health outcome is required.
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