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Data Analytics with Python All Weeks Assignment Solution
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Cryptography & Network Security All Week Assignment solution
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Excel Tricks
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State true or false: Statement: there is no difference between, E(y) = 0 + 1x and y = 0 + 1x + e , both are regression equations
True
False
Which of the following statements is correct:
Sensitivity in ROC analysis is called True Positive Rate(tpr)
Specificity in ROC analysis is not called True Negative Rate (tnr)
Specificity in ROC analysis is called True Positive Rate(tpr)
Sensitivity in ROC analysis is called True Negative Rate (tnr)
In ROC analysis when the Threshold value is Higher:
Specificity decreases
Sensitivity decreases
Both a. and b.
None of the above
Sensitivity in ROC analysis is defined as:
(Here, TP : True Positive, TN: True Negative, FP: False Positive, FN: False Negative)
FP/ (FP + TN)
FN / (TP + FN)
TN/ (TN + FP)
TP / (TP + FN)
In ROC analysis, a classifier is called ‘good’ if it has ______
Low TPR and Low FPR
Low TPR and High FPR
High TPR and Low FPR
High TPR and High FPR
For the given confusion matrix, compute the recall
True Positive True Negative
Predicted Positive 8 3
Predicted Negative 2 7
0.73
0.7
0.78
0.8
State true or False: Precision is inversely proportional to recall
True
False
State True or False: Standardization of features is not required before training a Logistic regression model
True
False
State True or False: Standardization of features is not required before training a Logistic regression model
Linear Regression errors values have to be normally distributed but in the case of Logistic Regression it is not the case
Logistic Regression errors values have to be normally distributed but in the case of Linear Regression it is not the case
Both Linear Regression and Logistic Regression error values have to be normally distributed
Both Linear Regression and Logistic Regression error values have not to be normally distributed
Which of the following is true regarding the logistic function for any value “x”?
Logistic(x): is a logistic function of any number “x”
Logit(x): is a logit function of any number “x”
Logit_inv(x): is an inverse logit function of any number “x”
Logistic(x) = Logit(x)
Logistic(x) = Logit_inv(x)
Logit_inv(x) = Logit(x)
None of these
