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Explain key ML/stats concepts
This interview assesses your foundational knowledge in Machine Learning and Statistics through a series of conceptual questions. Please address the following topics:
1. Convolutional Neural Networks (CNNs) vs. Recurrent Neural Networks (RNNs)
- What kinds of input structures do CNNs and RNNs each assume?
- Provide 1–2 examples of tasks where CNNs are typically preferred, and 1–2 examples where RNNs (or other sequence models) are preferred.
2. Gini Impurity (Decision Trees)
- Define Gini impurity for a node with class probabilities $p_1, \dots, p_K$.
- Compute the Gini impurity for a binary node with $p=0.8$ and $1-p=0.2$.
3. Entropy vs. Gini Impurity for Split Criteria
- Define entropy for a node and compare it to Gini impurity.
- Explain how they differ in sensitivity and whether they usually produce meaningfully different trees in practice.
4. Multicollinearity (Linear/Logistic Regression)
- What is multicollinearity, and why is it a problem in regression models?
- Name at least two ways to detect it and two ways to mitigate it.
5. Pearson Correlation
- Define Pearson correlation and list its key assumptions and limitations.
- Explain why correlation does not imply causation, and provide one example of confounding.
AI interview prep
Core dilemma
Balancing the breadth of fundamental ML/Stats knowledge with the depth of understanding practical implications and critical thinking.
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 key ML/stats concepts
This interview assesses your foundational knowledge in Machine Learning and Statistics through a series of conceptual questions. Please address the following topics:
1. Convolutional Neural Networks (CNNs) vs. Recurrent Neural Networks (RNNs)
- What kinds of input structures do CNNs and RNNs each assume?
- Provide 1–2 examples of tasks where CNNs are typically preferred, and 1–2 examples where RNNs (or other sequence models) are preferred.
2. Gini Impurity (Decision Trees)
- Define Gini impurity for a node with class probabilities $p_1, \dots, p_K$.
- Compute the Gini impurity for a binary node with $p=0.8$ and $1-p=0.2$.
3. Entropy vs. Gini Impurity for Split Criteria
- Define entropy for a node and compare it to Gini impurity.
- Explain how they differ in sensitivity and whether they usually produce meaningfully different trees in practice.
4. Multicollinearity (Linear/Logistic Regression)
- What is multicollinearity, and why is it a problem in regression models?
- Name at least two ways to detect it and two ways to mitigate it.
5. Pearson Correlation
- Define Pearson correlation and list its key assumptions and limitations.
- Explain why correlation does not imply causation, and provide one example of confounding.