What is the main goal of bias-variance tradeoff in deep learning?Question 10AnswerA.To minimize both bias and variance simultaneouslyB.To find the best-fitting model with the lowest bias and varianceC.To minimize the training errorD.To achieve perfect accuracy on the training data
Question
What is the main goal of bias-variance tradeoff in deep learning?Question 10AnswerA.To minimize both bias and variance simultaneouslyB.To find the best-fitting model with the lowest bias and varianceC.To minimize the training errorD.To achieve perfect accuracy on the training data
Solution
The main goal of the bias-variance tradeoff in deep learning is B. To find the best-fitting model with the lowest bias and variance.
Here's a step-by-step explanation:
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Bias and variance are two sources of error in machine learning models. Bias refers to the error due to the model's assumptions in the learning algorithm. High bias can cause the model to miss relevant relations between features and target outputs (underfitting).
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Variance, on the other hand, refers to the error due to the model's sensitivity to fluctuations in the training set. High variance can cause the model to model the random noise in the training data (overfitting).
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The bias-variance tradeoff is the balance that must be achieved between these two errors. An ideal model has low bias (capturing the patterns in the data well) and low variance (not overly sensitive to fluctuations in the data).
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Therefore, the main goal of the bias-variance tradeoff in deep learning is to find the best-fitting model that minimizes both bias and variance simultaneously. This helps to ensure that the model generalizes well to new, unseen data.
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