IBM Data Science Practice Test

Which of the following estimators are available to you if you choose a multiclass classification tree in Watson Studio?

1. Decision tree classifier

2. Random forest classifier

3. Naive Bayes

4. All of the above

The correct answer is: All of the above

Choosing a multiclass classification tree in Watson Studio provides access to a range of estimators that are effective for handling classification tasks. The decision tree classifier is a fundamental method that constructs a model based on the features of the data to categorize instances into multiple classes. It works by splitting the data into branches using decision rules, which is particularly suited for multiclass outcomes. The random forest classifier, an ensemble method based on multiple decision trees, adds robustness and improves prediction accuracy by averaging the results from numerous trees. This method also effectively manages multiclass classification by leveraging the collective decision-making of various trees, reducing the risk of overfitting compared to a single decision tree. Naive Bayes, while typically associated with a probabilistic approach to classification, can also be adapted for multiclass problems. It calculates the probability of a class given the features, making it suitable for scenarios with more than two categories. Since all three estimators—decision tree classifier, random forest classifier, and Naive Bayes—are capable of handling multiclass classification tasks, it confirms that the choice that encompasses all these estimators is valid. This diversity of tools allows data scientists to select the most appropriate method depending on their specific data characteristics and requirements, making the answer to the question