What does a high variance in a model typically indicate?*1 pointo A) The model is overfitting the datao B) The model is underfitting the datao C) The model has a high biaso D) The model has low flexibility
Question
What does a high variance in a model typically indicate?*1 pointo A) The model is overfitting the datao B) The model is underfitting the datao C) The model has a high biaso D) The model has low flexibility
Solution
A high variance in a model typically indicates that the model is overfitting the data. So, the answer is A) The model is overfitting the data.
Here's why:
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Variance refers to the amount by which our model would change if we estimated it using a different training dataset.
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When the variance is high, it means our model is highly sensitive to changes in our input data.
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This sensitivity often leads to overfitting, where the model performs well on the training data but poorly on unseen data. This is because it's capturing the noise and outliers in the training data along with the underlying pattern.
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So, a high variance is an indication of overfitting.
Similar Questions
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
What does high bias indicate about a model's performance?Question 6AnswerA. The model is performing optimallyB.The model is overfitting the training dataC. The model is underfitting the training dataD.The model has a high variance
What does high bias in a machine learning model indicate?Review LaterThe model is overfittingThe model is underfittingThe model has high varianceThe model is perfectly fit
What is the consequence of a model having low bias and high variance? Overfitting Underfitting High generalization Low computational complexity
Which of the following statements about bias and variance are true? (Select TWO correct answers) A. High bias models are typically underfit. B. Overfitting tends to lead to models with high variance and low bias. C. You can usually optimize both bias and variance simultaneously by choosing a more complex model. D. You can usually optimize both bias and variance simultaneously by choosing better hardware with GPUs.
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