What is the the range of Gini value for 2-class scenario?
{0, 0.5} 
{0, (m-1)/m}
{0, 1}
{0, m/(m-1)}
{0, 0.5}
What is the range of the entropy value for 2-class scenario?
{0, log2(m)}
{0, 0.5}
{0, l}
None of the above.
{0, l}
What is the range of the entropy value for m-class scenario?
{0, log2(m)}
{0, l}
{0, 0.5}
None of the above.
{0, log2(m)}
Like linear regression logistic regression………….
has one or more independent variables. 
provides a value directly from an equation for the dependent variable 
Uses the same method to estimate b weights.
has a dependent variable.
has one or more independent variables.
In a binary case, a cutoff of 0.5 means that cases with an estimated probability of P(Y=1) > 0.5 are classified as belonging to:
Class 1
Class 0
Both A and B
None of the above
Class 1
In logistic regression  
The model chi square (traditional fit measure/likelihood ratio test) provides information on overall model fit where a significant p value indicates that the current model is a better fit than the previous one.
The model chi square (traditional fit measure/likelihood ratio test) provides information on overall model fit where a significant p value indicates that the current model is a worse fit than the previous one.
The model chi square (traditional fit measure/likelihood ratio test) provides information on model fit for a single b coefficient, where a significant p value indicates that the current model is a better fit than the previous one.
The model chi square (traditional fit measure/likelihood ratio test) provides information on model fit for a single b coefficient, where a insignificant p value indicates that the current model is a better fit than the previous one
The model chi square (traditional fit measure/likelihood ratio test) provides information on overall model fit where a significant p value indicates that the current model is a better fit than the previous one.
Which of the following statement(s) can be true post adding a variable in a linear regression model?
R-Squared and Adjusted R-squared both increase
R-Squared increases and Adjusted R-squared decreases
R-Squared decreases and Adjusted R-squared decreases
R-Squared decreases and Adjusted R-squared increases
R-Squared and Adjusted R-squared both increase
Which of the above statement(s) are correct? Suppose you are training a linear regression model. Now consider these points. 1. Overfitting is more likely if we have less data 2. Overfitting is more likely when the hypothesis space is small.
Both are False
1 is False and 2 is True
1 is True and 2 is False
Both are True
1 is True and 2 is False
Regression trees are often used to model _______ data.
linear
nonlinear
categorical
symmetrical
nonlinear
Simple regression assumes a __________ relationship between the input  attribute and output attribute.
linear
quadratic
reciprocal
inverse
linear