Descriptive Analytics
What happened
Predictive Analytics
What will happen
Prescriptive Analytics
What action(s) would be
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algorithm
Step-by-step procedure designed to carry out a task.
change detection
Identifying when a significant change has taken place in a process.
classification
The separation of data into two or more categories, or (a point's classification) the category a data point is put into.
classifier
A boundary that separates the data into two or more categories. Also (more generally) an algorithm that performs classification.
cluster
A group of points identified as near/similar to each other.
cluster center
In some clustering algorithms (like ๐-means clustering), the central point (often the centroid) of a cluster of data points.
clustering
Separation of data points into groups ("clusters") based on nearness/similarity to each other. A common form of unsupervised learning.
cusum
Change detection method that compares observed distribution mean with a threshold level of change. Short for "cumulative sum".
deep learning
Neural network-type model with many hidden layers.
dimension
A feature of the data points (for example, height or credit score). (Note that there is also a mathematical definition for this word.)
EM algorithm
Expectation-maximization algorithm.
General description of an algorithm with two steps (often iterated), one that finds the function for the expected likelihood of getting the response given current parameters, and one that finds new parameter values to maximize that probability.
heuristic
Algorithm that is not guaranteed to find the absolute best (optimal) solution.
k-means algorithm
Clustering algorithm that defines ๐ clusters of data points, each corresponding to one of ๐ cluster centers selected by the algorithm.
K-Nearest Neighbor (KNN)
Classification algorithm that defines a data point's category as a function of the nearest ๐๐ data points to it.
kernel
A type of function that computes the similarity between two inputs; thanks to what's (really!) sometimes known as the "kernel trick", nonlinear classifiers can be found almost as easily as linear ones.
learning
Finding/discovering patterns (or rules) in data, often that can be applied to new data.
margin
For a single point, the distance between the point and the classification boundary; for a set of points, the minimum distance between a point in the set and the classification boundary. Also called the separation.
machine learning
Use of computer algorithms to learn and discover patterns or structure in data, without being programmed specifically for them
misclassified
Put into the wrong category by a classifier. [Show Less]