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. – Computer Science

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Select appropriate loss function → B. Train the model → C. Evaluate model performance using classification metrics → D. Adjust model parameters based on evaluation results

Train the model → A. Select appropriate loss function → C. Evaluate model performance using classification metrics → D. Adjust model parameters based on evaluation results

Evaluate model performance using classification metrics → D. Adjust model parameters based on evaluation results → A. Select appropriate loss function → B. Train the model

Adjust model parameters based on evaluation results → B. Train the model → A. Select appropriate loss function → C. Evaluate model performance using classification metrics

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Answer

First, choose a loss function that fits a multi‑class problem, such as categorical cross‑entropy, because it tells the optimizer how far predictions are from the true labels. Next, train the model on the labeled data, letting the loss guide weight updates. After training, assess the model with classification metrics like accuracy, precision, recall, and F1‑score to see how well it distinguishes each class. Finally, use those metric results to tweak hyperparameters or architecture, then repeat training if necessary. For example, if accuracy is low on a minority class, you might add class weights or increase training epochs to improve performance.

Detailed Explanation

Choosing the right loss function first tells the model how to learn. Other options are incorrect because Starting with training assumes a loss is already chosen, which can misguide learning; Evaluating before training gives no data to measure.

Key Concepts

Loss functions in classification

Model evaluation metrics

Multi-class classification

Topic

Classification Summary

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medium level question

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understand

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Question 2

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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Question 4

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Question 5

In multi-class classification, the primary objective of using multi-class loss functions is to evaluate the model's performance by penalizing incorrect predictions through various mechanisms, such as ______ loss, which is particularly effective in optimizing probabilistic outputs.

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Question 6

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Question 7

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