Learning Path
Question & Answer
Choose the Best Answer
The model is too complex and overfitting the training data.
The dataset is too small to capture the underlying patterns.
The loss function chosen is inappropriate for the task.
The parameters were selected randomly.
Understanding the Answer
Let's break down why this is correct
When a model is very flexible, it can learn the random noise in the training set instead of the true pattern. Other options are incorrect because A small dataset can make learning harder, but it does not explain why a model that fits the training data well still fails; Choosing a different loss function can change the training goal, but it does not prevent a complex model from overfitting.
Key Concepts
Empirical Risk Minimization
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Deep Dive: Empirical Risk Minimization
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Definition
Empirical risk minimization (ERM) is a method for selecting the best parameters for a predictive model by minimizing the average loss over a given dataset. ERM aims to find the parameters that provide the best fit to the training data based on a chosen loss function.
Topic Definition
Empirical risk minimization (ERM) is a method for selecting the best parameters for a predictive model by minimizing the average loss over a given dataset. ERM aims to find the parameters that provide the best fit to the training data based on a chosen loss function.
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