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You have a dataset with 20 classes.

How many classifiers will you have to train when you use the one versus one strategy to train a linear SVM?

You have a dataset with 20 classes.

How many classifiers will you have to train when you use the one versus all strategy to train a linear SVM?

To limit the influence of noise (erroneous samples) when training a k-nearest neighbor classifier, what should you do?

When training a linear SVM model, the C meta-parameter weights the cost of classification errors (versus the margin).

What happens if you increase the value of C meta-parameter?

Among the following properties, which ones are true regarding decision trees?

You have a dataset with 20 classes.

How many classifiers will you have to train when you use the one versus all strategy to train a decision tree?

Among the following properties, which are the required ones for a test set?

Linear regression is sensitive to noise (outliers in training data) because of what?

Select the problem each approach can solve.