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What is the role of the loss function in supervised learning?
Practice Questions
Q1
What is the role of the loss function in supervised learning?
To measure the accuracy of the model
To quantify the difference between predicted and actual values
To optimize the model's parameters
To select features for the model
Questions & Step-by-Step Solutions
What is the role of the loss function in supervised learning?
Steps
Concepts
Step 1: In supervised learning, we have a model that makes predictions based on input data.
Step 2: We also have actual values (the correct answers) that we want our model to predict.
Step 3: The loss function measures how far off the model's predictions are from the actual values.
Step 4: A lower loss value means the model's predictions are closer to the actual values.
Step 5: During training, the model uses the loss function to understand how to improve its predictions.
Step 6: The model adjusts its parameters to minimize the loss, which helps it learn better.
No concepts available.
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