Senior Data Scientist Associate Interview Questions and Answers

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

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