Q1. What’s the difference between Type-I and Type-II errors?
Ans. Type I error is a false positive, while Type II error is a false negative. Briefly stated, a Type I error means claiming something has happened when it hasn’t, while a Type II error means that you claim nothing is happening when in fact something is. A clever way to think about this is to think of Type I error as telling a man he is pregnant, while Type II error means you tell a pregnant woman she isn’t carrying a baby.

Q2. How is a Decision Tree pruned?
Ans. Pruning is what happens in decision trees when branches that have weak predictive power are removed in order to reduce the complexity of the model and increase the predictive accuracy of a decision tree model. Pruning can happen bottom-up and top-down, with approaches such as reduced error pruning and cost complexity pruning.
Reduced error pruning is perhaps the simplest version: replace each node. If it doesn’t decrease predictive accuracy, keep it pruned. While simple, this heuristic actually comes pretty close to an approach that would optimize for maximum accuracy.

Q3. What’s your favorite algorithm, and can you explain it to me in less than a minute?
Ans. I like this question because this gives me enough time to talk about my skills and stretch the interview. This is a good thing because the more you communicate with your interviewer, the more efficient time you spend in an interview, greater your chances of getting selected.

Q4. Name an example where ensemble techniques might be useful.
Ans. Ensemble techniques use a combination of learning algorithms to optimize better predictive performance. They typically reduce overfitting in models and make the model more robust (unlikely to be influenced by small changes in the training data). You could list some examples of ensemble methods, from bagging to boosting to a “bucket of models” method and demonstrating how they could increase predictive power.

Q5. Explain the difference between L1 and L2 regularization.
Ans. The main intuitive difference between the L1 and L2 regularization is that L1 regularization tries to estimate the median of the data while L2 regularization tries to estimate the mean of the data to avoid overfitting. That value will also be the median of the data distribution mathematically.

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