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Logistic Regression Interview Questions & Answers
Table of Content
 Classification analysis is a process of finding a function which divides the dataset into ______ based on different _________?
 Using ____________ we build the ROC curve (Receiver Operator characteristic) curve.
 What type of chart should we use, if we have estimated a set of data and want to plot the uncertainty of estimation?
 Which of the following is the true statement for supervised Models?
 Which of the following is a good dataset characteristic?
 F1 Score tries to find the balance between Precision and Recall?
 When performing regression or classification, which of the following is the right way to process the data?
 Increase the number of training examples in logistic regression will eventually decrease the Bias and increase the variance?
 Application of where classification is used?
 Both Null & Residual deviance is called _________.
 Is classification a supervised machine learning algorithm?
 A supervised learning (discrete target) model, also referred to as a classification model, can be evaluated using metrics such as _______?
 What is the output of the following?
 Which of the following is a true statement for logistic Regression? How to measure the accuracy of logistic regression?
 What are the assumptions of logistic regression?
 F1 Score is used to measure?
 Classification Accuracy is what we usually mean?

Classification analysis is a process of finding a function which divides the dataset into ______ based on different _________?
 a) Dependent based on different Independent
 b) Classes based on different Parameters
 c) Both 1 and 1
 d) None of the above
Answer  b) Classes based on different Parameters

Using ____________ we build the ROC curve (Receiver Operator characteristic) curve.
 a) Confusion matrix
 b) Akaike Information Criterion (AIC) value
 c) Multiple AIC Values
 d) None of the above
Answer  a) Confusion matrix

What type of chart should we use, if we have estimated a set of data and want to plot the uncertainty of estimation?
 a) Heat map
 b) Contour plot
 c) Error bar plot
 d) 3D Surface
Answer  c) Error bar plot

Which of the following is the true statement for supervised Models?
Statement 1: Classification analysis is a process of finding a function that divides the dataset into classes based on different parameters.
Statement 2: Regression analysis is a process of finding the correlation between dependent and independent variables in predicting a continuous value. a) Statement 1 is true and statement 2 is false
 b) Statement 1 is False and statement 2 is true
 c) Both Statement (1 & 2) is wrong
 d) Both Statement (1 & 2) is true
Answer  d) Both Statement (1 & 2) is true

Which of the following is a good dataset characteristic?
 a) Large enough to yield meaningful result
 b) Is representative of the dataset as a whole
 c) Both 1 and 2
 d) None of the above
Answer  c) Both 1 and 2

F1 Score tries to find the balance between Precision and Recall?
 a) True
 b) False
Answer  a) True

When performing regression or classification, which of the following is the right way to process the data?
 a) Normalize > PCA > Training
 b) PCA > Normalize > Training
 c) Normalize > PCA > Normalize PCA o/p > Training
 d) None of the above
Answer  a) Normalize > PCA > Training

Increase the number of training examples in logistic regression will eventually decrease the Bias and increase the variance?
 a) True
 b) False
 c) None of the above
 d) The given statement is wrong
Answer  b) False

Application of where classification is used?
 a) Customer churn prediction
 b) Handwriting Recognitions
 c) Breast cancer Detection
 d) All of the above
Answer  d) All of the above

Both Null & Residual deviance is called _________.
 a) Underfitting
 b) Overfitting
 c) Goodness of fit
 d) None of the above
Answer  c) Goodness of fit

Is classification a supervised machine learning algorithm?
 a) True
 b) False
Answer  a) True

A supervised learning (discrete target) model, also referred to as a classification model, can be evaluated using metrics such as _______?
 a) R Square, Adjusted R square, MSPE and MSAE
 b) Precision, Recall, Accuracy and ROCAUC
 c) CV error, BLEU Score
 d) Mutual Information
Answer  b) Precision, Recall, Accuracy and ROCAUC

What is the output of the following?
x = [‘ab’, ‘cd’]
y= (tuple(x))
print(x,y) a) [‘ab’, ‘cb’] (‘ab’, ‘cd’)
 b) ([‘a’, ‘b’], [‘c’, ‘d’])
 c) ([‘a’, ‘b’], [‘c’, ‘d’])
 d) [[‘a’, ‘b’,‘c’, ‘d’]]
Answer  a) [‘ab’, ‘cb’] (‘ab’, ‘cd’)

Which of the following is a true statement for logistic Regression? How to measure the accuracy of logistic regression?
Statement 1: Root means the square error is used to measure the accuracy of the classification problems
Statement 2: Where the prediction is < 0.5 there the predicted variable = 0. Where the prediction is >= 0.5 there the predicted variable = 1. a) Statement 1 is true and statement 2 is false
 b) Statement 1 is False and statement 2 is true
 c) Both Statement (1 & 2) is true
 d) None of them
Answer  b) Statement 1 is False and statement 2 is true

What are the assumptions of logistic regression?
 a) Linearity
 b) No endogeneity, No autocorrelation
 c) Normality and homoscedasticity, No multicollinearity
 d) All the above
Answer  d) All the above

F1 Score is used to measure?
 a) Test’s accuracy
 b) Train accuracy
 c) Validation data
 d) All the above
Answer  a) Test’s accuracy

Classification Accuracy is what we usually mean?
 a) It is the ratio of the number of correct predictions to the total number of input samples.
 b) True Positive Rate corresponds to the proportion of positive data points that are correctly considered as positive, with respect to all positive data points.
 c) False Positive Rate corresponds to the proportion of negative data points that are correctly considered as negative, with respect to all negative data points.
 d) False Positive Rate corresponds to the proportion of negative data points that are mistakenly considered as positive, with respect to all negative data points.
Answer  a)It is the ratio of the number of correct predictions to the total number of input samples.