Choose the correct statement with respect to prediction performance:
The focus is on continuous outcome variable
The focus is on categorical outcome variable
Prediction performance is same as classification performance
All of the above 
The focus is on continuous outcome variable
Consider the following statements and choose the correct option: Statement I: ‘Goodness of fit’ focuses on how well the model fits the data Statement II: The model obtained in data mining is certainly same as the one obtained in statistical setting
Only statement I is correct
Only statement II is correct
Both the statements are correct
None of the statement are correct
Only statement I is correct
In data mining modelling, prediction error is typically computed on:
Training partition
Validation partition
Test partition
None of the above
Validation partition
If the overall average error is negative, what does it indicate?
On an average level, the model is over predicting the observations
On an average level, the model is under predicting the observations
On an average level, the model has less impact on predicting the observations
The model has no impact on predicting the observations
On an average level, the model is under predicting the observations
Consider the following statements and choose the correct option: Assertion: RMSE is one of the most preferred metrics of predictive accuracy Reasoning: It is computed in the same unit as the outcome variable and similar to the standard error of estimate
Assertion is correct and reasoning gives the correct explanation
Assertion is correct but reasoning does not give the correct explanation
Assertion is incorrect but reasoning is correct
Both assertion and reasoning are incorrect
Assertion is correct and reasoning gives the correct explanation
Predictive modelling in multiple linear regression is:
Used to estimate best-fit model
Used to estimate values of outcome variable for new records
More emphasized in statistical setting
Prediction performance is of secondary importance
Used to estimate values of outcome variable for new records
Select the correct option:
OLS computes the sample estimates which minimizes the sum of deviations between predicted and actual values
OLS computes the sample estimates which maximizes the sum of deviations between predicted and actual values
OLS computes the sample estimates which minimizes the sum of squared deviation between predicted and actual values
OLS computes the sample estimates which maximizes the sum of squared deviation between predicted and actual values
OLS computes the sample estimates which minimizes the sum of squared deviation between predicted and actual values
Which of the following assumptions of multiple linear regression can be relaxed in data mining?
Noise follows a normal distribution
Observations are independent
Linear relationship holds true
Heteroscedasticity 
Noise follows a normal distribution
Which among the following statements is incorrect with respect to explanatory modeling?
The final model selected may not have the best predictive accuracy
Full sample is used to estimate the final model
More suitable for statistical modelling
There is no previously assumed structure
There is no previously assumed structure
Which of the following visualization techniques can be used for measuring predictive performance of a model?
Decile chart
Histogram
Lift curve
Both A and C
Both A and C