COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Low bias, high variance
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High bias, low variance.
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Low bias, low variance.
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High bias, low variance.
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Detailed explanation-1: -You have generated data from a 3-degree polynomial with some noise. What do you expect of the model that was trained on this data using a 5-degree polynomial as function class? High bias, low variance.
Detailed explanation-2: -If we fit the lower degree polynomial less than 3 which means that we have less complex model so in this case high bias and low variance. But in case of degree 3 polynomial it will have low bias and low variance.
Detailed explanation-3: -25) What do you expect will happen with bias and variance as you increase the size of training data? As we increase the size of the training data, the bias would increase while the variance would decrease.
Detailed explanation-4: -variance is an error from sensitivity to small fluctuations in the training set. High variance can cause an algorithm to model the random noise in the training data, rather than the intended outputs (overfitting) .”
Detailed explanation-5: -We can determine whether a predictive model is underfitting or overfitting the training data by looking at the prediction error on the training data and the evaluation data. Your model is underfitting the training data when the model performs poorly on the training data.