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Some examples from this set of practice questions
1.
What is the difference between Multiclass classification and Multilabel classification?
Answer: Multiclass classification makes the assumption that each sample is assigned to ONE label. Multilabel classification assigns to each sample a set of target labels.
2.
What is the Mean absolute error? (MAE)
Answer: The average (absolute) difference between true value and predicted value
3.
What is the disadvantage of using the \'error rate\' for classification problems?
Answer: The different types of errors are not taken into account
4.
What is the difference between precision and recall?
Answer: Precision refers to the fraction of relevant instances among the retrieved instances. Recall refers to the fraction of the total amount of relevant instances that were actually retrieved.
5.
What is the F-score?
Answer: The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0.
6.
What is the difference between \'micro-average\' and \'macro-average\'?
Answer: A macro-average will compute the metric independently for each class and then take the average (hence treating all classes equally), whereas a micro-average will aggregate the contributions of all classes to compute the average metric.
7.
True or False? The number of possible decision trees grows exponentially with number of features.
Answer: True
8.
What is the relationship between \'the base case\' and \'the recursive call\'? (decision trees)
Answer: The base case returns a value without making any subsequent recursive calls (= leaf node). If the base case is false, you will enter the recursive condition, which is the case when the function calls itself.
9.
What is \'Entropy\'?
Answer: Entropy is a measure of uncertainty, where more uniform distributions have more uncertainty.
10.
What is Gini impurity?
Answer: How often a random element would be labelled incorrectly if labels were assigned at random from a given distribution. It reaches its minimum (zero) when all cases in the node fall into a single target category.
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