MathLogic Analyst Interview Questions, Process, and Tips

Q1. What are the evaluation metrics for the classification ML model?
Ans. 
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
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Q2. When to use precision and recall
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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
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Q3. Regression evaluation metrics
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Q4. How to handle null values
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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)
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Q5. Random forest v/s XGBoost
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Q6. Window functions and aggregate functions in SQL
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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()
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Q7. Index and match functions v/s Vlookup
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Q8. 4 to 5 probability questions and puzzles
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General aptitude question and reasoning questions

Q1. Prepare coding , probability and ms excel
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Q2. Probablity question and coding questions
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