CodeHS Artificial Intelligence and Machine Learning Fundamentals 2

Question 1

What is the difference between artificial intelligence (AI) and machine learning (ML)?

Accepted Answer

AI is the broad field of making machines simulate intelligence; ML is a subset using data-driven learning

Answer

AI is the broader discipline of creating intelligent machines, while ML is a specific approach within AI that uses algorithms and data to enable systems to learn.

Question 2

Which evaluation metric measures the proportion of correctly classified instances out of all instances in a classification problem?

Accepted Answer

Accuracy

Answer

Accuracy is calculated as the number of correct predictions divided by the total number of predictions, representing the overall correctness of a classifier.

Question 3

What is a 'feature' in the context of a machine learning dataset?

Accepted Answer

An individual measurable property or characteristic used as input to the model

Answer

Features are the individual input variables or attributes (e.g., age, height, pixel value) provided to a machine learning model to make predictions.

Question 4

In CodeHS's AI and ML units, what concept describes an AI system's inability to perform well on data it has not seen during training?

Accepted Answer

Poor generalization

Answer

Poor generalization refers to a model's failure to apply learned patterns to new, unseen data, often caused by overfitting or insufficient training data.

Question 5

Which type of machine learning algorithm is a decision tree?

Accepted Answer

Supervised learning algorithm

Answer

Decision trees are supervised learning algorithms that learn a hierarchy of if/else rules from labeled training data to make classification or regression decisions.

(CodeHS) Code High School Certifications Practice Test

(CodeHS) Code High School Certifications Practice Test

CodeHS Artificial Intelligence and Machine Learning Fundamentals 2