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Beschreibung
Quiz von Vishakha Achmare, aktualisiert more than 1 year ago
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Erstellt von Vishakha Achmare
vor fast 4 Jahre
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Frage 1
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Regression analysis is a form of predictive modelling technique which investigates the relationship between a dependent and independent variable.
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True
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False
Frage 2
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Linear regression is a basic and commonly used type of predictive analysis which usually works on continuous data.
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True
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False
Frage 3
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Explain the equation: Y(predicted) = (β1*x + βo) + Error value
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write your answers down
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check them later
Frage 4
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write your answer down
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check time later
Frage 5
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The main goal of Gradient descent is to minimize the cost value. i.e. min J(θo, θ1)
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True
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False
Frage 6
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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.
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True
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False
Frage 7
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This general equation is for?
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Linear Regression
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Polynomial Regression
Frage 8
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Advantages of using Polynomial Regression are:
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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.
Frage 9
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Disadvantages of using Polynomial Regression are:
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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.
Frage 10
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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.
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one
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two
Frage 11
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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.
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True
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False
Frage 12
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Non-random pattern of the residual plot indicates that the model is,
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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
Frage 13
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Characteristics of a residue are:
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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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