Machine Learning 101 Practice Test: Decision Trees — Flashcards

Quiz on important decision trees concepts including decision tree pruning, inductive bias, classification trees, regression trees, and the powerful Random Forest algorithm.
 

Decision trees are a type of machine learning algorithm that split a dataset based on specific parameters until a final decision is made. They are one of the most easily explainable types of machine learning models. 

Here are some basics about decision trees:
Pruning: A technique that simplifies decision trees by reducing the rules. This helps to avoid complexity and improves accuracy.
Splitting: Decision trees split on different nodes until an outcome is obtained.
Parent and child nodes: When a node gets divided further, that node is termed as a parent node. The divided nodes or the sub-nodes are termed as a child node of the parent node.
Supervised machine learning algorithms: Decision trees are used in both classification and regression predictive modeling.
Hyperparameter tuning: An essential part of the machine-learning process that involves optimizing the model's performance by fine-tuning its hyperparameters.
Overfitting: The biggest issue of decision trees in machine learning. Overfitting causes high variance in outputs, which causes errors in the final decisions and inaccuracy in results.