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Logistic Regression Interview Questions & Answers [For Freshers & Experienced]


Logistic regression, a fundamental algorithm in machine learning for classification tasks, plays a pivotal role in predictive analytics. Its simplicity and effectiveness make it a go-to choice for beginners,,,,,,, in the field of data science and machine learning. This guide provides a set of logistic regression interview questions and detailed answers to help individuals prepare for interviews and deepen their understanding of this essential algorithm.

Understanding Logistic Regression

  1. What is logistic regression?

    Logistic regression is a statistical analysis technique used for binary classification. It predicts the likelihood of an event by applying a sigmoid function to the output of linear regression. This approach allows,,,,,,,BlogBloomhub.comstudying the relationship between independent variables, considering past information, and forecasting the probability of a dependent variable resulting in one of two outcomes.


    Logistic regression is employed to predict binary outcomes, such as whether a customer will make a purchase based on various features.

  2. Is logistic regression a descriptive or generative classifier? Why?

    Logistic regression is a descriptive classifier. It learns to categorize instances by understanding the,,,,,,,  that differentiate classes. Unlike generative classifiers, such as Naive Bayes, logistic regression doesn’t store all class characteristics but focuses on features that separate classes during classification.

  3. What do you mean by a decision boundary?

    A decision boundary is a line or hyperplane that divides classes in a classification model. In logistic regression,,,,,,,, the algorithm aims to find a boundary that enables accurate predictions by segregating data based on features.

Logistic Regression Interview Questions

Question Set 1: True/False Questions

  1. Is logistic regression a type of a supervised machine learning algorithm?

    Answer: True. Logistic regression is a supervised machine learning algorithm as it requires labeled instances (target variable) for training.

  2. Is logistic regression mainly used for classification?

    Answer: True. Logistic regression is primarily used for classification tasks, not regression. It utilizes a sigmoid activation function for classification.

  3. Can a neural network mimic the behavior of a logistic regression algorithm?

    Answer: True. Neural networks, as universal approximators, can emulate the behavior of logistic regression by adding a layer with a sigmoid activation function.

  4. Can logistic regression solve a multi-class classification problem directly?

    Answer: False. Logistic regression alone can’t solve multi-class problems directly. One approach is to use a one vs. all strategy or opt for more complex algorithms.

Question Set 2: Specific Knowledge Questions

  1. What method is used to fit the training data in logistic regression?

    Answer: Maximum Likelihood. Logistic regression uses Maximum Likelihood, not Least Square error, to fit training data.

  2. Which metric cannot be used to measure the correctness of a logistic regression model?

    Answer: Mean Squared Error (MSE). Logistic regression being a classification algorithm, MSE is not suitable for evaluating its performance.


Preparing for logistic regression interview questions is crucial for those entering the field of data science or machine learning. This comprehensive guide offers a range of questions and detailed answers to enhance understanding and readiness for interviews. As the landscape of data science evolves,,,,,,,, having a solid grasp of logistic regression becomes increasingly vital for aspiring professionals.

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