Senior Data Scientist Associate

Senior Data Scientist Associate Interview Questions and Answers

Updated 7 Mar 2025

Q1. How does the xgboost algorithm works , with explanation on every steps

Ans.

XGBoost is an efficient implementation of gradient boosting that optimizes performance and accuracy through ensemble learning.

1. **Gradient Boosting Framework**: XGBoost builds models in a sequential manner, where each new model corrects errors made by the previous ones.
2. **Decision Trees**: It primarily uses decision trees as base learners, where each tree is built to minimize the loss function.
3. **Regularization**: XGBoost includes L1 (Lasso) and L2 (Ridge) regularization...read more

Ans.

This program reverses an array containing both integers and strings.

Use a loop to iterate through the array from the last index to the first.
Create a new array to store the reversed elements.
Example: For input ['apple', 1, 'banana', 2], output should be [2, 'banana', 1, 'apple'].

Ans.

Linear programming optimizes a linear objective function subject to linear constraints, finding the best outcome in a feasible region.

Linear programming involves maximizing or minimizing a linear objective function.
Constraints are linear inequalities that define the feasible region.
The feasible region is typically a convex polygon in multi-dimensional space.
The Simplex method is a popular algorithm used to solve linear programming problems.
An example: Maximizing profit from p...read more

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Ans.

ML metrics help evaluate model performance, each with trade-offs affecting accuracy, interpretability, and application.

Accuracy vs. Precision: High accuracy may come with low precision in imbalanced datasets. Example: Classifying rare diseases.
Recall vs. F1 Score: High recall may lower F1 score, impacting balance in precision and recall. Example: Fraud detection.
ROC-AUC vs. PR-AUC: ROC-AUC is sensitive to class imbalance, while PR-AUC focuses on positive class performance. Ex...read more

Ans.

Evaluation metrics are used to assess the performance of a model during training.

Common evaluation metrics include accuracy, precision, recall, F1 score, and ROC-AUC.
Accuracy measures the proportion of correctly classified instances out of the total instances.
Precision measures the proportion of true positive predictions out of all positive predictions.
Recall measures the proportion of true positive predictions out of all actual positive instances.
F1 score is the harmonic mea...read more

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Ans.

MAPE measures the percentage error, MSE and RMSE measure the average squared error, R2 measures the proportion of variance explained.

MAPE (Mean Absolute Percentage Error) measures the percentage error between actual and predicted values.
MSE (Mean Squared Error) measures the average squared difference between actual and predicted values.
RMSE (Root Mean Squared Error) is the square root of MSE, providing a more interpretable metric.
R2 (Coefficient of Determination) measures the...read more