Explain key ML/stats concepts

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

1. 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.

1. 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).

1. 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.

1. 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.

1. Pearson correlation

• Define Pearson correlation and list key assumptions/limitations.
• Explain why correlation (\neq) causation and give one example of confounding.

Clarifying Questions to Ask

• Clarify the task, data shape, labels, constraints, and evaluation metric.
• State assumptions behind the math or modeling technique you choose.
• Connect theory to practical training, debugging, and deployment implications.