APPLICATION OF SUPERVISED LEARNING
LINEAR REGRESSION
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly.What do you expect will happen with bias and variance as you increase the size of training data?
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Bias increases and Variance increases
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Bias decreases and Variance increases
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Bias decreases and Variance decreases
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Bias increases and Variance decreases
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Explanation:
Detailed explanation-1: -A Neural network which is a component of deep learning, can be used as a universal approximator, so it can definitely implement a linear regression algorithm.
Detailed explanation-2: -Therefore, the answer is A. Linear regression is sensitive to outliers.
Detailed explanation-3: -1-Increasing training data size leads to decrease variance. 2-Decrease the variance leads to increase the bias. so increase the training data size leads to decrease variance and increase variance.
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