Terms and definitions from all courses

Dot notation: How to access the methods and attributes that belong to an instance of a class


Downsampling: The process of removing some observations from the majority class, making it so they make up a smaller percentage of the dataset than before Dummy variables: Variables with values of 0 or 1 that indicate the presence or absence of something


dtype: A NumPy attribute used to check the data type of the contents of an array


Dynamic typing: Variables that can point to objects of any data type


Dynamic value: A value the user inputs or the output of a program, an operation, or a function

E

Econometrics: A branch of economics that uses statistics to analyze economic problems Edge computing: A way of distributing computational tasks over a bunch of nearby processors (i.e., computers) that is good for speed and resiliency and does not depend on a single source of computational power


elif: A reserved keyword that executes subsequent conditions when the previous conditions are not true


else: A reserved keyword that executes when preceding conditions evaluate as False
Empirical probability: A type of probability based on experimental or historical data Empirical rule: A concept stating that the values on a normal curve are distributed in a regular pattern, based on their distance from the mean


Ensemble learning: Refers to building multiple models and aggregating their predictions Ensembling: (Refer to ensemble learning)


Enumerate(): A built-in function that iterates through a sequence and tracks each element and its place in the index


eps (Epsilon): In DBSCAN clustering models, a hyperparameter that determines the radius of a search area from any given point