Terms and definitions from all courses

min_samples_leaf: In decision tree and random forest models, a hyperparameter that defines the minimum number of samples for a leaf node called “min_child_weight” in XGBoost

min_samples_split: In decision tree and random forest models, a hyperparameter that defines the minimum number of samples that a node must have to split into more nodes
Missing data: A data value that is not stored for a variable in the observation of interest
Mode: The most frequently occurring value in a dataset
Model assumptions: Statements about the data that must be true in order to justify the use of a particular modeling technique
Model selection: The process of determining which model should be the final product and put into production
Model validation: The set of processes and activities intended to verify that models are performing as expected
Modularity: The ability to write code in separate components that work together and that can be reused for other programs
Module: A simple Python file containing a collection of functions and global variables Modulo: An operator that returns the remainder when one number is divided by another


MSE (Mean Squared Error): The average of the squared difference between the predicted and actual values
Multiple linear regression: A technique that estimates the relationship between one continuous dependent variable and two or more independent variables


Multiple regression: (Refer to multiple linear regression)
Multiplication rule (for independent events): The concept that if the events A and B are independent, then the probability of both A and B happening is the probability of A multiplied by the probability of B
Mutability: The ability to change the internal state of a data structure


Mutually exclusive: The concept that two events are mutually exclusive if they cannot occur at the same time