COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Overfitting
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Underfitting
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Low bias
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High bias
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Detailed explanation-1: -A low bias model incorporates fewer assumptions about the target function. A linear algorithm often has high bias, which makes them learn fast. In linear regression analysis, bias refers to the error that is introduced by approximating a real-life problem, which may be complicated, by a much simpler model.
Detailed explanation-2: -Examples of high-bias machine learning algorithms include: Linear Regression, Linear Discriminant Analysis and Logistic Regression.
Detailed explanation-3: -Low-Bias, High-Variance: This is a case of overfitting where model predictions are inconsistent and accurate on average. The predicted values will be accurate(average) but will be scattered. High-Bias, Low-Variance: This is a case of underfitting where predictions are consistent but inaccurate on average.
Detailed explanation-4: -Definition. Model Bias (also Algorithmic Bias) denotes the systematic and repeatable error in a Risk Model that creates outcomes that are statistically at odds with the system, population or behavior that is being modeled.