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Explain leakage, missing data, and common losses
Data Leakage, Missing Data, and Regression Losses
This question assesses fundamental Machine Learning knowledge across three key areas:
-
Data Leakage:
- Define data leakage.
- Provide 2-3 common examples of data leakage.
- Describe practical methods for preventing or mitigating data leakage.
-
Missing Data Handling:
- Discuss common strategies for dealing with missing values in a dataset.
- Explain the trade-offs between dropping rows/columns and imputing missing values.
- How can the pattern of missingness itself be a valuable signal?
-
Linear vs. Logistic Regression Losses:
- Identify the typical loss function used for linear regression.
- Identify the typical loss function used for logistic regression.
- Compare Mean Squared Error (MSE) and Mean Absolute Error (MAE), highlighting their differences and scenarios where one might be preferred over the other.
AI interview prep
Core dilemma
Balancing model performance and robustness against the practical challenges of real-world data imperfections (leakage, missing values) and the fundamental mathematical properties of different regression tasks.
Mid-level signals3
Senior signals3
Red flags3
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Explain leakage, missing data, and common losses
Data Leakage, Missing Data, and Regression Losses
This question assesses fundamental Machine Learning knowledge across three key areas:
-
Data Leakage:
- Define data leakage.
- Provide 2-3 common examples of data leakage.
- Describe practical methods for preventing or mitigating data leakage.
-
Missing Data Handling:
- Discuss common strategies for dealing with missing values in a dataset.
- Explain the trade-offs between dropping rows/columns and imputing missing values.
- How can the pattern of missingness itself be a valuable signal?
-
Linear vs. Logistic Regression Losses:
- Identify the typical loss function used for linear regression.
- Identify the typical loss function used for logistic regression.
- Compare Mean Squared Error (MSE) and Mean Absolute Error (MAE), highlighting their differences and scenarios where one might be preferred over the other.