Terms and definitions from all courses

Errors: In a regression model, the natural noise assumed to be in a model
Escape character: A character that changes the typical behavior of the characters that follow it


Execute stage: Stage of the PACE workflow where a data professional will present findings with internal and external stakeholders, answer questions, consider different viewpoints, and make recommendations


Explanatory variable: (Refer to independent variable)
Explicit conversion: The process of converting a data type of an object to a required data type


Exploratory data analysis (EDA): The process of investigating, organizing, and analyzing datasets and summarizing their main characteristics, often by employing data wrangling and visualization methods; the six main practices of EDA are: discovering, structuring, cleaning, joining, validating, and presenting
Expression: A combination of numbers, symbols, or other variables that produce a result when evaluated
Extra Sum of Squares F-test: Quantifies the difference between the amount of variance that is left unexplained by a reduced model that is explained by the full model Extracting: The process of retrieving data out of data sources for further data processing


Extrapolation: A model’s ability to predict new values that fall outside of the range of values in the training data

F

F1-Score: The harmonic mean of precision and recall
False positive: A test result that indicates something is present when it really is not
Feature engineering: The process of using practical, statistical, and data science knowledge to select, transform, or extract characteristics, properties, and attributes from raw data Feature extraction: A type of feature engineering that involves taking multiple features to create a new one that would improve the accuracy of the algorithm Feature selection: A type of feature engineering that involves selecting the features in the data that contribute the most to predicting the response variable Feature transformation: A type of feature engineering that involves modifying existing features in a way that improves accuracy when training the model