Which of the following statements regarding model estimation bias and variance is false?Group of answer choicesEstimation bias typically is caused by small sample sizes.ML algorithms with more model complexity tend to have higher estimation variance.High estimation bias is when an ML algorithm can’t fit the true pattern, leading to underfitting.Model estimation variance can be reduced by adding more data or averaging different models.

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 does the term "estimation bias" refer to?Group of answer choicesA measure of model flexibility that causes the estimated model to be sensitive to data nuancesA measure of model rigidity that prevents adaptation to nuances of the dataA measure of model rigidity that causes the estimated model to be sensitive to data nuances

What is the consequence of a model having low bias and high variance? Overfitting Underfitting High generalization Low computational complexity

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

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.)