d. All of the above

Designing a learning system comes with several challenges, including:

a. Overfitting: This occurs when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. This means that the noise or random fluctuations in the training data is picked up and learned as concepts by the model.

b. Underfitting: This happens when a model cannot adequately capture the underlying structure of the data. An underfit model will have poor performance on the training data.

c. Bias-Variance tradeoff: Bias is the simplifying assumptions made by the model to make the target function easier to approximate. Variance is the amount that the estimate of the target function will change given different training data. The tradeoff is that as you increase the complexity of your model, you will decrease bias but increase variance; and vice versa.

Therefore, all of the above are key challenges in designing a learning system.

Question

d. All of the above

Designing a learning system comes with several challenges, including:

a. Overfitting: This occurs when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. This means that the noise or random fluctuations in the training data is picked up and learned as concepts by the model.

b. Underfitting: This happens when a model cannot adequately capture the underlying structure of the data. An underfit model will have poor performance on the training data.

c. Bias-Variance tradeoff: Bias is the simplifying assumptions made by the model to make the target function easier to approximate. Variance is the amount that the estimate of the target function will change given different training data. The tradeoff is that as you increase the complexity of your model, you will decrease bias but increase variance; and vice versa.

Therefore, all of the above are key challenges in designing a learning system.

Knowee AI · Accepted Answer

d. All of the above

Designing a learning system comes with several challenges, including:

a. Overfitting: This occurs when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. This means that the noise or random fluctuations in the training data is picked up and learned as concepts by the model.

b. Underfitting: This happens when a model cannot adequately capture the underlying structure of the data. An underfit model will have poor performance on the training data.

c. Bias-Variance tradeoff: Bias is the simplifying assumptions made by the model to make the target function easier to approximate. Variance is the amount that the estimate of the target function will change given different training data. The tradeoff is that as you increase the complexity of your model, you will decrease bias but increase variance; and vice versa.

Therefore, all of the above are key challenges in designing a learning system.

Q.No 5. What is a key challenge in designing a learning system?a. Overfittingb. Underfittingc. Bias-variance tradeoffd. All of the above

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Q.No 5. What is a key challenge in designing a learning system?

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