MathLogic Interview Questions, Process, and Tips

Evaluation metrics for classification ML model

• Accuracy: measures the overall correctness of the model

• Precision: measures the proportion of true positive predictions among all positive predictions

• Recall: measures the proportion of true positive predictions among all actual positives

• F1 Score: harmonic mean of precision and recall

• Confusion Matrix: provides a summary of correct and incorrect predictions

Precision and recall are used in evaluating the performance of classification models.

• Precision is used when the goal is to minimize false positives.

• Recall is used when the goal is to minimize false negatives.

• Precision is calculated as TP / (TP + FP) where TP is true positives and FP is false positives.

• Recall is calculated as TP / (TP + FN) where FN is false negatives.

• In medical field, precision is important when identi...read more

Null values can be handled by either removing them, replacing them with a default value, or imputing them with a calculated value.

• Remove null values from the dataset

• Replace null values with a default value (e.g. 0 for numerical data, 'Unknown' for categorical data)

• Impute null values with a calculated value (e.g. mean, median, mode)

Window functions and aggregate functions in SQL

• Window functions perform calculations across a set of table rows related to the current row

• Aggregate functions perform a calculation on a set of values and return a single value

• Examples of window functions include ROW_NUMBER(), RANK(), and LAG()

• Examples of aggregate functions include SUM(), AVG(), and COUNT()