Data Glossary Lab

Cross-Validation is a resampling method used to evaluate the performance of a machine learning model by ensuring it can generalize to new, unseen data.

Computer Science (CS) is a multifaceted field of study focused on the theoretical and practical aspects of processing information in digital computers, the design of computer hardware and software, and the diverse applications of computing technology.

A Continuous Variable is a type of quantitative variable that can take on an infinite set of numerical values within a specific range.

Computer vision is an area of computer science concerned with enabling computers to achieve high-level understanding from digital images or videos.

Cost function is a machine learning function used to measure the average of the differences between the predicted and actual values over the training set, and supposed to be minimized.

A confusion matrix is a table illustrating the predictive performance of a classification model, showing true positives, true negatives, false positives, and false negatives.

A categorical variable is a variable that can have one of a limited number of possible values (categories) without any intrinsic ordering involved.

Classification is a supervised learning problem when it is necessary to predict categorical outcomes based on input features. Examples of classification problems are fraud detection and email spam filters. Commonly used classification algorithms are k-nearest neighbors, decision trees, random forest, etc.

Clustering is an unsupervised learning problem concerned with grouping all the observations of a dataset according to their similarity by some common characteristics. Common clustering algorithms are k-means, hierarchical clustering, spectral clustering, etc.