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10+ Rifle Factory, Ishapore Interview Questions and Answers

Updated 4 May 2024

Q1. What is the difference between linear and logistic reg.?

Ans.

Linear regression is used for predicting continuous values, while logistic regression is used for predicting binary outcomes.

  • Linear regression is used when the target variable is continuous and has a linear relationship with the independent variables.

  • Logistic regression is used when the target variable is binary or categorical and the relationship between independent variables and the target is non-linear.

  • Linear regression predicts the value of a continuous outcome variable b...read more

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Q2. What is Logistic Regression? What is Recall

Ans.

Logistic Regression is a statistical method used to model the probability of a certain event occurring based on one or more predictor variables. Recall is a metric used to evaluate the performance of a classification model.

  • Logistic Regression is used for binary classification tasks, where the outcome variable is categorical and has two classes.

  • It estimates the probability that a given input belongs to a certain category.

  • Recall, also known as sensitivity, is the ratio of true ...read more

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Q3. What are the performance metrics of Regression

Ans.

Performance metrics of Regression include Mean Squared Error, R-squared, Mean Absolute Error, etc.

  • Mean Squared Error (MSE)

  • R-squared (R^2)

  • Mean Absolute Error (MAE)

  • Root Mean Squared Error (RMSE)

  • Adjusted R-squared

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Q4. What is Recall?

Ans.

Recall is a metric used in classification models to measure the ability of the model to find all relevant cases within a dataset.

  • Recall is calculated as the number of true positive predictions divided by the sum of true positive and false negative predictions.

  • It is also known as sensitivity or true positive rate.

  • A high recall indicates that the model is good at identifying all relevant cases, even if it means more false positives.

  • For example, in a medical diagnosis model, hig...read more

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Q5. Explain confidance interval

Ans.

Confidence interval is a range of values that is likely to contain the true population parameter with a certain level of confidence.

  • Confidence interval is used to estimate the population parameter based on a sample statistic.

  • It is calculated by taking the sample mean and adding or subtracting the margin of error.

  • The level of confidence is typically set at 95% or 99%.

  • For example, a 95% confidence interval for the average height of a population might be 65-75 inches.

  • The wider t...read more

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Q6. Explain academic projects

Ans.

Academic projects are assignments given to students to apply their theoretical knowledge in practical scenarios.

  • Academic projects are designed to help students gain hands-on experience in their field of study.

  • They require students to apply their theoretical knowledge to solve real-world problems.

  • Examples of academic projects include building a website, designing a software application, conducting a research study, or creating a prototype of a product.

  • Academic projects often i...read more

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Q7. Explain logistic regression

Ans.

Logistic regression is a statistical method used to analyze and model the relationship between a binary dependent variable and one or more independent variables.

  • It is used to predict the probability of a binary outcome (0 or 1).

  • It uses a sigmoid function to map the input values to the output probability.

  • It is commonly used in machine learning for classification problems.

  • Example: predicting whether a customer will buy a product or not based on their age, gender, income, etc.

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Q8. Use of confusion matrix

Ans.

Confusion matrix is a table used to evaluate the performance of a classification model.

  • It helps in calculating various metrics like accuracy, precision, recall, F1 score, etc.

  • It compares the predicted values with the actual values to identify true positives, true negatives, false positives, and false negatives.

  • It is commonly used in machine learning and data science to evaluate the performance of classification models.

  • Example: In a binary classification problem, a confusion m...read more

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Q9. What if you get failed in convincing the banker?

Ans.

I would analyze the reasons for the failure and try to address them in my next attempt.

  • I would ask for feedback from the banker to understand their concerns.

  • I would research and gather more information to strengthen my argument.

  • I would try to build a relationship with the banker to gain their trust.

  • I would explore alternative options and present them to the banker.

  • I would seek advice from colleagues or mentors to improve my approach.

  • I would remain persistent and continue to f...read more

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Q10. What is defect life cycle?

Ans.

Defect life cycle is the process of identifying, reporting, fixing, and verifying defects in software development.

  • Defect is identified by testers during testing

  • Defect is reported to the development team

  • Development team fixes the defect

  • Fixed defect is verified by testers

  • Defect is closed if it passes verification

  • If defect fails verification, it is reopened and the cycle continues

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Q11. What ECP & BVA?

Ans.

ECP stands for Equivalence Class Partitioning and BVA stands for Boundary Value Analysis.

  • ECP is a testing technique that divides input data into groups that are expected to exhibit similar behavior.

  • BVA is a testing technique that focuses on testing values at the boundaries of input domains.

  • ECP and BVA are used to identify defects in software and improve the quality of testing.

  • For example, if a program accepts input values between 1 and 100, BVA would test values at the lower ...read more

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Q12. Test scenarios for ATM

Ans.

Test scenarios for ATM

  • Verify if the ATM is dispensing the correct amount of cash

  • Check if the ATM is accepting valid cards and rejecting invalid ones

  • Test if the ATM is able to handle multiple transactions at the same time

  • Ensure that the ATM is providing proper instructions to the user

  • Validate if the ATM is able to handle network connectivity issues

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