This is a quiz on machine learning using decision trees and random forest models, implemented with Scikit-learn.  Also covered is hyperparameter tuning.

Decision Trees and Random Forests

Decision trees are a _____________ and _____________ method of Machine Learning.

Decision trees are able to do which of the following types of machine learning problems?  

In Scikit-lern, Can decision trees and random forests can handle Categorical values?

Decision trees are not impacted by outliers in the data.  

Which statement below best describes the relationship between hyperparameters and parameters?  

When working with tree models, what considerations need to be taken into account with respect to scaling and standardization of features?

Which of the following are the advantages of a decision tree model over linear models?

Bagging tries to reduce the variance of a model.

Which of the following are true about the random forest algorithm?

Which of the following statements are true about Gini index?

The goal of the feature selection while building a decision tree is to find features that lead to a split in children nodes which has ________ entropy

A node in a decision tree is said to be pure if all the data instances belong to the ___ class

The goal of feature selection while working with decision tree is to find the feature that results in ___________ information gain

Match the following sentences with their values

a. Entropy value of ______ represents that the data sample is pure or homogenous

b. Entropy value of ______ represents that the data sample has a 50-50 split belonging to two categories

1. 0
2. 1



A decision tree created with the aim to achieve very minimal entropy may result in _______

Which of the following are true regarding the Random forest technique?

What is bootstrapping in machine learning?

Is feature scaling required for tree-based models?

Which of the following statements are not true about Random Forest models?

Is a random forest model impacted by outliers?

When no extra hyperparameters are specified, a Scikit-learn decision tree will use how many features when deciding each split? (note n_features is the total number of features in the data set)

Test your knowledge about Machine Learning using Decision Trees and Random Forests in this quiz