IntellectFaces Technology Solutions Interview Questions and Answers

Q1. Difference between bagging and boosting
Ans. 
Bagging and boosting are ensemble learning techniques used to improve model performance.
• Bagging involves training multiple models on different subsets of the training data and combining their predictions through averaging or voting.

• Boosting involves iteratively training models on the same data, with each subsequent model focusing on the errors of the previous model.

• Bagging reduces overfitting and variance, while boosti...read more
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Q2. How would you measure model effectiveness without using any of confusion matrix metrics given the data is highly imbalanced
Ans. 
One way to measure model effectiveness without using confusion matrix metrics is by using area under the receiver operating characteristic curve (AUC-ROC).
• Calculate the AUC-ROC score to evaluate the model's ability to distinguish between positive and negative classes.

• AUC-ROC considers the entire range of classification thresholds and is insensitive to class imbalance.

• Higher AUC-ROC score indicates better model performa...read more
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Q3. What is Blue score in Regression
Ans. 
Blue score is not a term used in regression analysis.
• Blue score is not a standard term in regression analysis

• It is possible that the interviewer meant to ask about another metric such as R-squared or mean squared error

• Without further context, it is difficult to provide a more specific answer
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Quant, Reasoning and python based MCQs

Q1. Question on your past work experience. Can go deep down with respect to your prior work experience
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Q2. In what project have you been involved and what were your roles and responsibility
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Q3. Data Science project pipeline ,what components are involved , step by step process
Ans. 
Data science project pipeline involves multiple components and follows a step-by-step process.
• 1. Define the problem statement and objectives of the project.

• 2. Collect and preprocess the data needed for analysis.

• 3. Explore and visualize the data to gain insights.

• 4. Build and train machine learning models to solve the problem.

• 5. Evaluate the models using appropriate metrics.

• 6. Deploy the model into production and monitor...read more
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Q1. Your prior experience and salary expectations
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Q1. Questions on Data Analysis
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Q1. Questions to check the culture fit
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Q1. Sql, python, ml based questions
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