Arrange the following steps in the correct order of the Empirical Risk Minimization process: A) 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.

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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

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Answer

First, gather and clean the data so the model has a reliable training set. Next, choose a loss function that tells the model how far its predictions are from the true values. Then, adjust the model’s parameters to minimize that loss, usually with an optimization algorithm. Finally, test the tuned model on a separate validation set to see how well it generalizes. For instance, to predict house prices, you would collect housing data, pick mean‑squared error, train a regression, and evaluate it on a hold‑out set.

Detailed Explanation

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

Model Evaluation

Loss Functions

Topic

Empirical Risk Minimization

Difficulty

hard level question

Cognitive Level

understand

Practice Similar Questions

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Question 1

Arrange the following steps in the correct order for constructing a parametrized predictor model: 1) Define the structure of the predictor, 2) Choose the parameters, 3) Collect the data, 4) Optimize the parameters using empirical risk minimization.

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Question 2

Arrange the following steps in the process of evaluating a loss function in empirical risk minimization: A) Compute the predicted values using the predictor function, B) Determine the actual output values, C) Calculate the loss by comparing predicted and actual values, D) Use the loss to update the model parameters.

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Question 3

In the context of Empirical Risk Minimization, the process of selecting parameters that minimize the average loss is often referred to as __________.

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Question 4

Arrange the following steps in the correct order for evaluating a multi-class classification model using loss functions and metrics: A) Select appropriate loss function, B) Train the model, C) Evaluate model performance using classification metrics, D) Adjust model parameters based on evaluation results.

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