Struggling with machine learning concepts for AI-900? Discover how overall loss measures the gap between predictions and expected outcomes to ace your certification.
Table of Contents
Question
As per machine learning core concepts, which of the following correctly describes the overall loss in a machine learning model?
A. The size of the learning rate for the regression function
B. The sum of the features included in the cost function
C. The difference between the model’s predictions and expected values
D. The size of the dataset kept aside as a validation set
Answer
C. The difference between the model’s predictions and expected values
Explanation
The overall loss in a machine learning model is the difference between the model’s predictions and actual values. It refers to a single numerical value that summarizes the model’s performance on a given dataset, typically the validation set. It is calculated by summing individual loss values for each prediction made on the data points in the validation set. These individual loss values are typically computed using a loss function which quantifies the error between the model’s predictions and the actual target values.
The size of the dataset kept aside as a validation set does not describe the overall loss in a machine learning model. This refers to the amount of data used for evaluation but does not directly represent the overall loss. The loss is calculated based on the model’s performance on the validation data, not its size.
The sum of the features included in the cost function does not describe the overall loss in a machine learning model. The number of features simply refers to the quantity of independent variables used in the model. It might impact the model’s complexity and performance, but it does not relate to the overall loss, which focuses on the difference between predictions and actual values.
The size of the learning rate for the regression function does not describe the overall loss in a machine learning model. The learning rate controls the step size during model training and does not represent the overall loss which focuses on evaluating the model’s performance on unseen data.
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