EXL Service Data Scientist Interview Questions and Answers

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 performance.

• Example: A model with an AUC-ROC score of 0.85 perform...read more

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

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 boosting reduces bias and underfitting.

• Examples of bagging algor...read more

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 SMOTE (Synthetic Minority Over-sampling Technique) to generat...read more

Linear regression is used for continuous variables, while logistic regression is used for binary classification.

• Linear regression is used to predict continuous values, such as predicting house prices based on square footage.

• Logistic regression is used for binary classification, such as predicting whether an email is spam or not.

• Linear regression assumes a linear relationship between the independent and dependent variables, while logistic regression models the probability of a...read more

Predicting insurance claims using machine learning algorithms.

• Fraud detection in insurance claims

• Risk assessment for insurance policies

• Pricing optimization for insurance products

• Customer segmentation for targeted marketing

• Predictive maintenance for insurance assets