Chapter 7 – 50 Most Asked Logistic Regression Interview Questions
Topic – 50 Most Asked Logistic Regression Interview Questions
Welcome to the 2200 questions series from The Data Monk, in this series we will cover all the topics in a Question-Answer mode that are required for anyone who wants to make a career in the following field:-
– Data Analysis
– Business Analysis
– Business Intelligence Engineering
– Machine Learning
– Data Science
– Product Analysis
– Data Engineering
– Risk Analysis
These 2200 questions are useful for anyone who is in their 2nd-3rd year of engineering to 8-10 years of experience in the IT industry( be it QA/Development/Support) and are willing to make a career in Analytics.
Most Asked Logistic Regression Interview Questions
Why Analytics is a domain for you?
If you want to make a handsome switch with a good package then Analytics is for you because of the following reasons:-
– It is a high-paying job
– It is interesting as you will have a good impact on the growth of the organization
– It involves a lot of things like requirement gathering, building logic, making ETL, pipeline creation, reporting to the CXOs, and so on. So, it is a very impactful role
– It has a HUGE demand in the future as the data will keep on growing and so will your role
How much does an analytics role pay?
The CTC of the role will definitely depend on multiple factors but just to give you a glimpse of it:-
“Anyone from a tier 2-3 college with good knowledge of the material that we are providing will have a fair chance to bag something like 15+ LPA for a fresher. The more you grind the better you get and the CTC grows with experience.”
Now coming back to why you should try The Data Monk for your Analytics journey.
Why The Data Monk?
We are a group of 30+ Analytics Engineers working in various product-based companies like Zomato, Ola, OYO, Google, Rapido, Uber, Ugam, BYJUs, etc. and we observed that people do not have a well-structured way to enhance their knowledge. There are multiple courses here and there, but no one has consolidated what needs to be learned in order to move to the analytics domain.
Further, there are courses from Large institutes where they charge you something like 2-5 lacks and try to teach you everything from Data structure to SQL to Power BI to ML. You do not have to spend so much on these topics.
We followed a very old-school way, take a topic and solve 100-200 questions on these topics. Learn them, understand them, and revise them. This should be enough for you to crack that domain.
For example, if I am a very beginner in SQL, then I will just try to solve 200 questions starting from the definition to advance level questions. After solving and revising these questions I should have a good amount of knowledge to answer 6 out of 10 questions asked in an interview and going by that calculation I can be a strong candidate in 5-7 out of 10 companies.
See, by the end, you need to convert a job first and then keep on learning in the organization.
Most of the books are on questions like ‘250 questions to crack SQL interview’ and this will cost you around 250 rupees, take the book, understand, and learn it. This small amount can bag you a 15 LPA job 🙂
You can trust us as we have guided more than 1000 people to make a career in Analytics
2200 Analytics Interview Questions
Chapter 1 – SQL –Â 250 SQL questions to Ace any Analytics Intervie
Chapter 2 – Python – 200 Most Asked Python Interview Questions
Chapter 3 – Pandas – 100 Most Asked Pandas Interview Questions with Solution
Chapter 4 – Numpy – 100 Most Asked Numpy Interview Questions with solution
Chapter 5 – Case Study and Guesstimate – 100 Case Study and Guesstimate with a complete solution
Chapter 6 -Linear Regression – 50 Most Asked Linear Regression Interview Questions with solution
Chapter 7 – Logistic Regression – 50 Most Asked Logistic Regression Interview Questions with solution
801. What is Classification?
802.Why is logistic regression called regression if it does the job of classification?
803. What is Logistic Regression?
804. What is the similarity between linear regression and logistic regression?
805. Explain the mechanism of Logistic Regression
806. What are the applications of Logistic Regression?
807. What are between Logistic Regression and Linear Regression?
808. What is a Sigmoid Function?
809. What is the difference between Sigmoid function and SoftMax function?
810. In a nutshell, Explain the advantages and disadvantages of Logistic Regression?
811. What are the assumptions of Logistic Regression?
812. Why does the response variable in the data should be binary in nature when using the logistic regression algorithm?
813. What is Hypothesis test?
814. What is Log Transformation?
815. What is the general workflow of Logistic Regression Algorithm?
816. What are the libraries required for implementing Logistic Regression?
817 How to import the dataset into the python Environment?
818. The dataset does not has headers, how to define the headers on the dataset?
819. How can you revert the original data frame in case any failure in your data analysis?
820. How to drop some of the variables in the dataset?
821. How to have look at the dataset?
822. How to check the null values in the dataset?
823. How to impute the missing values?
824. How to know the count of occurrences of the variables?
826. How to convert the categorical data into numerical data?
827. On what basis should we decide that outliers should be eliminated or
not ??
828. How to check for outliers in the data?
829. Explain Label Encoder
830. Explain Manual Mapping
831. What are Dummy Variables?
832. Explain One Hot Label Encoding
833. Explain the steps of Label Encoding?
834. What is Scaling?
835. Explain the syntax of Scaling and which scaling technique we will be using in this algorithm?
836. How will we decide how to train the model and how to test the model on the data which is available to us?
837. What is the threshold for splitting the data?
838. What do you mean by feature splitting?
839. What do you mean by feature selection?
840. What is loc and iloc in python and what is the difference between them?
841. What is the code for building the Logistic Regression Model?
842. Can we create a custom function of Confusion Matrix so that we can picturize it beautifully?
843. How to check whether our model is performing well or not?
844. What is Accuracy of model?
845. What is Precision of model?
846. What is Recall Factor ?
847. Does Logistic Regression provide any feature in these condition where
the recall factor is not satisfying?
848. What is the difference between SVM and Logistic Regression?
849. What is ROC?
850. What is AUC?
851. What is the code for plotting ROC curve?
852. How to calculate the AUC scores?
853. What is SGD Stochastic Gradient Descent Classifier?
854. What is the difference between (SGD)Stochastic Gradient Descent Classifier and Logistic Regression?
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