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 variance
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High variance
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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: -It is impossible to have an ML model with a low bias and a low variance. When a data engineer modifies the ML algorithm to better fit a given data set, it will lead to low bias-but it will increase variance. This way, the model will fit with the data set while increasing the chances of inaccurate predictions.
Detailed explanation-3: -OLS has the lowest bias but highest variance.
Detailed explanation-4: -Examples of low-bias machine learning algorithms include: Decision Trees, k-Nearest Neighbors and Support Vector Machines. Examples of high-bias machine learning algorithms include: Linear Regression, Linear Discriminant Analysis and Logistic Regression.