Machine Learning Python Exam Prep Quiz

Supervised learning involves training a model on a labeled dataset, where the model learns to make predictions based on input-output pairs. Unsupervised learning, on the other hand, involves training a model on an unlabeled dataset, where the model learns to find patterns or structures in the data without specific input-output pairs.

The formula for Euclidean distance between two points with coordinates $(x_1, y_1)$ and $(x_2, y_2)$ is $d = \sqrt{(x_2 - x_1)^2 + (y_2 - y_1)^2}$. Euclidean distance is used in K-means clustering to measure the distance between data points and cluster centroids, helping to assign each data point to the nearest centroid for clustering.

Feature reduction in machine learning aims to reduce the dimensionality of the input data by selecting a subset of relevant features, which can improve model performance and reduce computational complexity. An example of a model used for feature reduction is Principal Component Analysis (PCA).

Clustering in machine learning involves grouping similar data points together based on certain criteria, such as distance or similarity. Different types of clustering include K-means clustering, hierarchical clustering, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), and Gaussian Mixture Models (GMM).

Data preprocessing in machine learning involves transforming raw data into a clean and organized format suitable for model training. It includes tasks such as data cleaning, normalization, encoding categorical variables, and handling missing values. Data preprocessing helps improve the quality of input data for better model performance and generalization.