K-nearest neighbor (KNN) is an algorithm that is used to classify a data point based on how its neighbors are classified. Here’s what you need to know.
Correlation occurs when two variables change at the same time, while causation is when a change in one variable causes the other to change. Here’s why you need to understand the difference.
A fully connected layer refers to a neural network in which each input node is connected to each output node. In a convolutional layer, not all nodes are connected. Here’s what you need to know.
Cohen’s kappa is a quantitative measure of reliability for two raters that are evaluating the same thing. Here’s what you need to know and how to calculate it.
