Terms and definitions from all courses

N

n_clusters: In K-means and agglomerative clustering models, a hyperparameter that specifies the number of clusters in the final model


N-dimensional array: The core data object of NumPy; also referred to as “ndarray”


n_estimators: In random forest and XGBoost models, a hyperparameter that specifies the number of trees your model will build in its ensemble
Naive Bayes: A supervised classification technique that is based on Bayes’s Theorem with an assumption of independence among predictors
Naming conventions: Consistent guidelines that describe the content, creation date, and version of a file in its name
Naming restrictions: Rules built into the syntax of a programming language
NaN: How null values are represented in pandas; stands for “not a number”
ndim: A NumPy attribute used to check the number of dimensions of an array


Negative correlation: An inverse relationship between two variables, where when one variable increases, the other variable tends to decrease, and vice versa Nested loop: A loop inside of another loop
No multicollinearity assumption: An assumption of simple linear regression stating that no two independent variables (Xi and Xj) can be highly correlated with each other


Non-null count: The total number of data entries for a data column that are not blank Non-probability sampling: A sampling method that is based on convenience or the personal preferences of the researcher, rather than random selection
None: A special data type in Python used to indicate that things are empty or that they return nothing
Nonprofit: A group organized for purposes other than generating profit; often aims to further a social cause or provide a benefit to the public
Nonresponse bias: Refers to when certain groups of people are less likely to provide responses