MACHINE LEARNING
INTRODUCTION
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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0
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1
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0.5
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None of these
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Detailed explanation-1: -In statistics, the mean squared error (MSE) is a risk function that measures the square of errors. When performing regression, use MSE if you believe your target is normally distributed and you want large errors to be penalized more than small ones.
Detailed explanation-2: -The closer your MSE value is to 0, the more accurate your model is. However, there is no ‘good’ value for MSE. It is an absolute value which is unique to each dataset and can only be used to say whether the model has become more or less accurate than a previous run.
Detailed explanation-3: -Linear regression analysis is used to predict the value of a variable based on the value of another variable. The variable you want to predict is called the dependent variable. The variable you are using to predict the other variable’s value is called the independent variable.