1. Bais and variance trade-off 2. How to handle Imbalanced data? 3. What is Multicollinearity and how do you handle it? 4. Explain Lasso & Ridge? 5. Difference between Bagging and Boosting. 6. Explain K-Means clusters and the Elbow method.

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Questions related to data analysis techniques and methods.

Bais and variance trade-off: balancing model complexity and accuracy
Handling imbalanced data: resampling techniques, adjusting class weights, using different evaluation metrics
Multicollinearity: when predictor variables are highly correlated, can be handled by feature selection or regularization
Lasso & Ridge: regularization techniques to prevent overfitting by adding penalty terms to the loss function
Bagging vs Boosting: ensemble methods to improve model performance by combining multiple models
K-Means clusters & Elbow method: unsupervised learning technique to group similar data points together, elbow method helps to determine optimal number of clusters

1. Bais and variance trade-off 2. How to handle Imbalanced data? 3. What is Multicollinearity and how do you handle it? 4. Explain Lasso & Ridge? 5. Difference between Bagging and Boosting. 6. Explain K-Means clusters and the Elbow method.

Top Cognizant Data Analyst interview questions & answers

Popular interview questions of Data Analyst

Top HR questions asked in Cognizant Data Analyst

1. Bais and variance trade-off 2. How to handle Imbalanced data? 3. What is Multicollinearity and how do you handle it? 4. Explain Lasso &amp; Ridge? 5. Difference between Bagging and Boosting. 6. Explain K-Means clusters and the Elbow method.

Top Cognizant Data Analyst interview questions & answers

Popular interview questions of Data Analyst

Top HR questions asked in Cognizant Data Analyst

1. Bais and variance trade-off 2. How to handle Imbalanced data? 3. What is Multicollinearity and how do you handle it? 4. Explain Lasso & Ridge? 5. Difference between Bagging and Boosting. 6. Explain K-Means clusters and the Elbow method.