Explain key ML/stats concepts

Q: Explain key ML/stats concepts

This is a Machine Learning interview question from C3 AI for Data Scientist roles. View the full question and solution on PracHub.

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Question

You are taking an ML/Stats screening with conceptual multiple-choice questions. Answer the following:

CNN vs. RNN
- What kinds of input structure does each model assume?
- Give 1–2 examples of tasks where CNNs are typically preferred and tasks where RNNs (or sequence models) are preferred.
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 $1-p=0.2$ .
Entropy vs. Gini 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.
Multicollinearity (linear/logistic regression)
- What is multicollinearity and why is it a problem?
- Name at least two ways to detect it and two ways to mitigate it.
Pearson correlation
- Define Pearson correlation and list key assumptions/limitations.
- Explain why correlation $\neq$ causation and give one example of confounding.

Explain key ML/stats concepts

Solution

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