Deloitte Data Scientist Interview Questions and Answers

DSA was asked. And general coding language questions were asked. Previous experience based questions were asked.

Machine Learning, Generative AI, Deep learning interview questions. 2 Coding problems based on Algorithms.

Q1. One systems design question was there.
Add your answer
Q2. Situations based problems was there.
Add your answer
Q3. Questions on Past experiences.
Add your answer

Q1. Questions related to regression
Add your answer
Q2. Feature selection methods
Ans. 
Feature selection methods help identify the most relevant variables for predictive modeling, improving model performance and interpretability.
• Filter methods: Use statistical tests (e.g., Chi-square, ANOVA) to select features based on their relationship with the target variable.

• Wrapper methods: Evaluate subsets of features by training a model (e.g., Recursive Feature Elimination) to find the best combination.

• Embedded me...read more
Answered by AI
Add your answer

Q1. What is the difference between precision and recall
Ans. 
Precision is the ratio of correctly predicted positive observations to the total predicted positives, while recall is the ratio of correctly predicted positive observations to the all observations in actual class.
• Precision focuses on the accuracy of positive predictions, while recall focuses on the proportion of actual positives that were correctly identified.

• Precision = TP / (TP + FP), Recall = TP / (TP + FN)

• High prec...read more
Answered by AI
Add your answer

1 hour test with 3 python programming questions.

Q1. Are all the decision trees same in a random forest ?
Ans. 
No, decision trees in a random forest are different due to the use of bootstrapping and feature randomization.
• Decision trees in a random forest are trained on different subsets of the data through bootstrapping.

• Each decision tree in a random forest also considers only a random subset of features at each split.

• The final prediction in a random forest is made by aggregating the predictions of all individual decision trees...read more
Answered by AI
Add your answer
Q2. How will you handle class imbalanced dataset to increase the f1 score ?
Ans. 
Handling class imbalanced dataset involves techniques like resampling, using different algorithms, adjusting class weights, and using ensemble methods.
• Use resampling techniques like oversampling the minority class or undersampling the majority class.

• Try using different algorithms that are less sensitive to class imbalance, such as Random Forest or XGBoost.

• Adjust class weights in the model to give more importance to the...read more
Answered by AI
Add your answer