28255362
Description
Quiz by Vishakha Achmare, updated more than 1 year ago
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Created by Vishakha Achmare
over 3 years ago
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Question 1
Question
Regression analysis is a form of predictive modelling technique which investigates the relationship between a dependent and independent variable.
Answer
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True
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False
Question 2
Question
Linear regression is a basic and commonly used type of predictive analysis which usually works on continuous data.
Answer
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True
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False
Question 3
Question
Explain the equation: Y(predicted) = (β1*x + βo) + Error value
Answer
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write your answers down
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check them later
Question 4
Answer
-
write your answer down
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check time later
Question 5
Question
The main goal of Gradient descent is to minimize the cost value. i.e. min J(θo, θ1)
Answer
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True
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False
Question 6
Question
Choosing a perfect learning rate is a very important task as it depends on how large of a step we take downhill during each iteration.
Answer
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True
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False
Question 7
Question
This general equation is for?
Answer
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Linear Regression
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Polynomial Regression
Question 8
Question
Advantages of using Polynomial Regression are:
Answer
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Polynomial provides the best approximation of the relationship between the dependent and independent variables.
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A broad range of functions can be fit under it.
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Polynomial basically fits a wide range of curvature.
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All of the above.
Question 9
Question
Disadvantages of using Polynomial Regression are:
Answer
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The presence of one or two outliers in the data can seriously affect the results of the nonlinear analysis.
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These are too sensitive to the outliers.
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In addition, there are unfortunately fewer model validation tools for the detection of outliers in nonlinear regression than there are for linear regression.
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All of the above.
Question 10
Question
simple linear regression is a type of regression analysis where the number of independent variables is ____ and there is a linear relationship between the independent(x) and dependent(y) variable.
Answer
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one
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two
Question 11
Question
Residual plot helps in analyzing the model using the values of residues. It is plotted between predicted values and residue. Their values are standardized. The distance of the point from 0 specifies how bad the prediction was for that value. If the value is positive, then the prediction is low. If the value is negative, then the prediction is high. 0 value indicates prefect prediction. Detecting residual pattern can improve the model.
Answer
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True
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False
Question 12
Question
Non-random pattern of the residual plot indicates that the model is,
Answer
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Missing a variable which has significant contribution to the model target
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Missing to capture non-linearity (using polynomial term)
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No interaction between terms in model
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All of the above
Question 13
Question
Characteristics of a residue are:
Answer
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Residuals do not exhibit any pattern
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Adjacent residuals should not be same as they indicate that there is some information missed by system.
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All of the above
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