# Logistic Regression Interview Questions – Part 3

**Q1. What is accuracy?**

Accuracy is the number of correct predictions out of all predictions made.

Accuracy=True Positives+True NegativesTotal Number of Predictions

**Q2. Why is accuracy not a good measure for classification problems?**

Accuracy
is not a good measure for classification problems because it gives
equal importance to both false positives and false negatives. However,
this may not be the case in most business problems. For example, in the
case of cancer prediction, declaring cancer as benign is more serious
than wrongly informing the patient that he is suffering from cancer.
Accuracy gives equal importance to both cases and cannot differentiate
between them.

**Q3. What is the importance of a baseline in a classification problem?**

Most
classification problems deal with imbalanced datasets. Examples include
telecom churn, employee attrition, cancer prediction, fraud detection,
online advertisement targeting, and so on. In all these problems, the
number of the positive classes will be very low when compared to the
negative classes. In some cases, it is common to have positive classes
that are less than 1% of the total sample. In such cases, an accuracy of
99% may sound very good but, in reality, it may not be.

Here, the negatives are 99%, and hence, the baseline will remain the same. If the algorithms predict all the instances as negative, then also the accuracy will be 99%. In this case, all the positives will be predicted wrongly, which is very important for any business. Even though all the positives are predicted wrongly, an accuracy of 99% is achieved. So, the baseline is very important, and the algorithm needs to be evaluated relative to the baseline.

**Q4. What are false positives and false negatives?**

False
positives are those cases in which the negatives are wrongly predicted
as positives. For example, predicting that a customer will churn when,
in fact, he is not churning.

False negatives are those cases in which the positives are wrongly predicted as negatives. For example, predicting that a customer will not churn when, in fact, he churns.

**Q5. What are the true positive rate (TPR), true negative rate (TNR), false positive rate (FPR), and false negative rate (FNR)?**

TPR
refers to the ratio of positives correctly predicted from all the true
labels. In simple words, it is the frequency of correctly predicted true
labels.

TPR=TPTP+FN

TNR refers to the ratio of negatives correctly predicted from all the false labels. It is the frequency of correctly predicted false labels.

TNR=TNTN+FP

FPR refers to the ratio of positives incorrectly predicted from all the true labels. It is the frequency of incorrectly predicted false labels.

FPR=FPTN+FP

FNR refers to the ratio of negatives incorrectly predicted from all the false labels. It is the frequency of incorrectly predicted true labels.

FNR=FNTP+FN

**Q6. What are sensitivity and specificity?**

Specificity
is the same as true negative rate, or it is equal to 1 – false positive
rate. It tells you out of all the actual ‘0’ labels, how many were
correctly predicted.

Specificity=TNTN+FP

Sensitivity is the true positive rate. It tells you out of all the actual ‘1’ labels, how many were correctly predicted.

Sensitivity=TPTP+FN

**Q7. What are precision and recall?**

Precision
is the proportion of true positives out of predicted positives. To put
it in another way, it is the accuracy of the prediction. It is also
known as the ‘positive predictive value’.

Precision=TPTP+FP

Recall is the same as the true positive rate (TPR) or the sensitivity.

Recall=TPTP+FN

**Q8. What is F-measure?**

It
is the harmonic mean of precision and recall. In some cases, there will
be a trade-off between the precision and the recall. In such cases, the
F-measure will drop. It will be high when both the precision and the
recall are high. Depending on the business case at hand and the goal of
data analytics, an appropriate metric should be selected.

F−measure=2×Precision×RecallPrecision+Recall

**Q9. Explain the use of ROC curves and the AUC of an ROC Curve.**

An
ROC (Receiver Operating Characteristic) curve illustrates the
performance of a binary classification model. It is basically a TPR
versus FPR (true positive rate versus false positive rate) curve for all
the threshold values ranging from 0 to 1. In an ROC curve, each point
in the ROC space will be associated with a different confusion matrix. A
diagonal line from the bottom-left to the top-right on the ROC graph
represents random guessing. The Area Under the Curve (AUC) signifies how
good the classifier model is. If the value for AUC is high (near 1),
then the model is working satisfactorily, whereas if the value is low
(around 0.5), then the model is not working properly and just guessing
randomly. From the image below, curve C (green) is the best ROC curve
among the three and curve A (brown) is the worst ROC curve among the
three.

ROC Curves

**Q10. How to choose a cutoff point in case of a logistic regression model?**

The
cutoff point depends on the business objective. Depending on the goals
of your business, the cutoff point needs to be selected. For example,
let’s consider loan defaults. If the business objective is to reduce the
loss, then the specificity needs to be high. If the aim is to increase
the profits, then it is an entirely different matter. It may not be the
case that profits will increase by avoiding giving loans to all
predicted default cases. But it may be the case that the business has to
disburse loans to default cases that are slightly less risky to
increase the profits. In such a case, a different cutoff point, which
maximises profit, will be required. In most of the instances, businesses
will operate around many constraints. The cutoff point that satisfies
the business objective will not be the same with and without
limitations. The cutoff point needs to be selected considering all these
points. If the business context doesn’t matter much and you want to
create a balanced model, then you use an ROC curve to see the tradeoff
between sensitivity and specificity and accordingly choose an optimal
cutoff point where both these values along with accuracy are decent.

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