The characteristic of a model with high variance is B. It performs well on the training data but poorly on unseen data.

Here's why:

High variance in a model indicates that the model is complex and has been trained to fit too closely to the specific details and noise in the training data. This is also known as overfitting.

While this complexity allows the model to perform very well on the training data (since it's essentially memorized the data), it often performs poorly on unseen data. This is because the noise and specific details it learned from the training data don't apply to new data, causing the model to make incorrect predictions.

So, a model with high variance will typically have a low training error (because it performs well on the training data) but a high test error (because it performs poorly on unseen data).

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The characteristic of a model with high variance is B. It performs well on the training data but poorly on unseen data.

Here's why:

High variance in a model indicates that the model is complex and has been trained to fit too closely to the specific details and noise in the training data. This is also known as overfitting.

While this complexity allows the model to perform very well on the training data (since it's essentially memorized the data), it often performs poorly on unseen data. This is because the noise and specific details it learned from the training data don't apply to new data, causing the model to make incorrect predictions.

So, a model with high variance will typically have a low training error (because it performs well on the training data) but a high test error (because it performs poorly on unseen data).

Knowee AI · Accepted Answer

The characteristic of a model with high variance is B. It performs well on the training data but poorly on unseen data.

Here's why:

High variance in a model indicates that the model is complex and has been trained to fit too closely to the specific details and noise in the training data. This is also known as overfitting.

While this complexity allows the model to perform very well on the training data (since it's essentially memorized the data), it often performs poorly on unseen data. This is because the noise and specific details it learned from the training data don't apply to new data, causing the model to make incorrect predictions.

So, a model with high variance will typically have a low training error (because it performs well on the training data) but a high test error (because it performs poorly on unseen data).

Which of the following is a characteristic of a model with high variance?Question 4AnswerA.It tends to underfit the training dataB.It performs well on the training data but poorly on unseen dataC.It has a low training error and a low test errorD.It has a high training error and a high test error

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