AML Supervised & Unsupervised Learning Algorithms

Question 1

What distinguishes supervised learning from unsupervised learning?

Accepted Answer

Supervised learning uses labeled data, unsupervised does not

Answer

The fundamental distinction between supervised and unsupervised learning lies in the nature of the data used for training. Supervised learning algorithms are trained on labeled datasets, meaning each data point is paired with a corresponding output or target variable. In contrast, unsupervised learning algorithms work with unlabeled data, aiming to discover hidden patterns or structures within the data without prior knowledge of outcomes.

Question 2

Which algorithm is commonly used in supervised learning?

Accepted Answer

Linear Regression

Answer

Linear Regression is a classic and widely used algorithm in supervised learning, specifically for regression tasks. It models the relationship between a dependent variable and one or more independent variables by fitting a linear equation to the observed data. This algorithm requires labeled data where the output variable is continuous, making it a prime example of a supervised approach.

Question 3

What is a common use case for unsupervised learning?

Accepted Answer

Customer segmentation

Answer

Customer segmentation is a common and powerful application of unsupervised learning. In this scenario, businesses have vast amounts of customer data but no predefined labels for customer groups. Unsupervised algorithms, such as clustering, can analyze this unlabeled data to identify natural groupings of customers with similar behaviors or characteristics, enabling targeted marketing and personalized strategies.

Question 4

Which of these is an unsupervised learning technique?

Accepted Answer

K-Means Clustering

Answer

K-Means Clustering is a prominent unsupervised learning technique used for partitioning a dataset into K distinct, non-overlapping subgroups or clusters. Unlike supervised methods, it does not require pre-labeled data; instead, it identifies inherent groupings based on the similarity of data points. This makes it ideal for tasks like customer segmentation or anomaly detection where labels are unknown.

Question 5

Which metric is typically used to evaluate classification algorithms?

Accepted Answer

Accuracy

Answer

Accuracy is a widely used and intuitive metric for evaluating classification algorithms. It measures the proportion of correctly predicted instances (both true positives and true negatives) out of the total number of instances in the dataset. While other metrics like precision, recall, and F1-score provide more nuanced insights, accuracy offers a straightforward overall measure of a classifier's performance.

(AML) Advanced Machine Learning Certification Practice Test

(AML) Advanced Machine Learning Certification Practice Test

AML Supervised & Unsupervised Learning Algorithms