Learning Path
Question & Answer
Choose the Best Answer
Select a loss function → B) Optimize the parameters of the model → C) Evaluate the model's performance on validation data → D) Collect and prepare the dataset
Collect and prepare the dataset → B) Select a loss function → C) Optimize the parameters of the model → D) Evaluate the model's performance on validation data
Optimize the parameters of the model → B) Collect and prepare the dataset → C) Select a loss function → D) Evaluate the model's performance on validation data
Evaluate the model's performance on validation data → B) Optimize the parameters of the model → C) Collect and prepare the dataset → D) Select a loss function
Understanding the Answer
Let's break down why this is correct
First, you gather and clean the data because the model needs examples to learn from. Other options are incorrect because This answer puts evaluation before data collection, which is like trying to taste a dish before you have any ingredients; This option suggests training before having data, which is impossible because the model has nothing to learn from.
Key Concepts
Empirical Risk Minimization
hard level question
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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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