EXL Service Data Scientist Interview Questions, Process, and Tips

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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Basic aptitude , tech aptitude

Q1. What is overfitting
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Q2. How to handle missing values
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Q1. Project related questions
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Q2. Coding questions
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Q1. Cross validation
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Q2. How to handle imbalanced dataset
Ans. 
Handling imbalanced datasets involves techniques like resampling, using different algorithms, and adjusting class weights.
• Use resampling techniques like oversampling the minority class or undersampling the majority class.

• Utilize algorithms that are robust to imbalanced datasets, such as Random Forest, XGBoost, or SVM.

• Adjust class weights in the model to give more importance to the minority class.

• Use techniques like SMO...read more
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