A simple model with few parameters is most likely to suffer from High Bias.

Here's why:

1. Bias is the simplifying assumptions made by the model to make the target function easier to approximate. In other words, bias is the error from erroneous assumptions in the learning algorithm. High bias can cause an algorithm to miss the relevant relations between features and target outputs (underfitting).

2. A model with few parameters is a simple model. Simple models assume that the data is linear or that features are independent. In reality, this is not often the case. Therefore, these models tend to oversimplify the problem, leading to high bias.

3. On the other hand, high variance is a result of a model that is too complex, such as a model with many parameters. These models are highly flexible and can fit the training data very well. However, they often perform poorly on unseen data because they overfit to the training data.

So, a simple model with few parameters is most likely to suffer from high bias, not high variance.

Question

A simple model with few parameters is most likely to suffer from High Bias.

Here's why:

1. Bias is the simplifying assumptions made by the model to make the target function easier to approximate. In other words, bias is the error from erroneous assumptions in the learning algorithm. High bias can cause an algorithm to miss the relevant relations between features and target outputs (underfitting).

2. A model with few parameters is a simple model. Simple models assume that the data is linear or that features are independent. In reality, this is not often the case. Therefore, these models tend to oversimplify the problem, leading to high bias.

3. On the other hand, high variance is a result of a model that is too complex, such as a model with many parameters. These models are highly flexible and can fit the training data very well. However, they often perform poorly on unseen data because they overfit to the training data.

So, a simple model with few parameters is most likely to suffer from high bias, not high variance.

Knowee AI · Accepted Answer

A simple model with few parameters is most likely to suffer from High Bias.

Here's why:

1. Bias is the simplifying assumptions made by the model to make the target function easier to approximate. In other words, bias is the error from erroneous assumptions in the learning algorithm. High bias can cause an algorithm to miss the relevant relations between features and target outputs (underfitting).

2. A model with few parameters is a simple model. Simple models assume that the data is linear or that features are independent. In reality, this is not often the case. Therefore, these models tend to oversimplify the problem, leading to high bias.

3. On the other hand, high variance is a result of a model that is too complex, such as a model with many parameters. These models are highly flexible and can fit the training data very well. However, they often perform poorly on unseen data because they overfit to the training data.

So, a simple model with few parameters is most likely to suffer from high bias, not high variance.

A simple model with few parameters is most likely to suffer from:Group of answer choicesHigh BiasHigh Variance

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