Which of the following is a way to diagnose bias and variance in a model?Question 5AnswerA.L1 regularizationB.Feature engineeringC.Cross-validationD.Gradient descent
Question
Which of the following is a way to diagnose bias and variance in a model?Question 5AnswerA.L1 regularizationB.Feature engineeringC.Cross-validationD.Gradient descent
Solution
The best way to diagnose bias and variance in a model from the given options is C. Cross-validation.
Here's why:
A. L1 regularization: This is a technique to avoid overfitting, but it doesn't directly help in diagnosing bias or variance.
B. Feature engineering: This is the process of creating new features or modifying existing features which can improve the performance of a model. However, it doesn't directly diagnose bias or variance.
C. Cross-validation: This is a powerful preventative measure against overfitting. The dataset is divided into 'k' groups or folds where each fold is used as a testing set at some point. This method gives an insight into how the model generalizes to an independent dataset. It gives us a clear picture of the bias/variance trade-off.
D. Gradient descent: This is an optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent. It's used to find the parameters (coefficients) for a model that minimize the error of the model on your training data, but it doesn't diagnose bias or variance.
Similar Questions
Explain the bias-variance tradeoff in machine learning. How do you handle it? (To Answer - speak your choice loudly and then logically explain your choice.)
What is the bias-variance tradeoff?Review LaterThe tradeoff between the accuracy and speed of a machine learning modelThe tradeoff between the complexity and interpretability of a machine learning modelThe tradeoff between the amount of bias and variance in a machine learning modelThe tradeoff between the quality and quantity of the training data
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
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.
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
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