Terms and definitions from all courses

Margin of error: The maximum expected difference between a population parameter and a sample estimate
Markdown: A markup language that lets the user write formatted text in a coding environment or plain-text editor
matplotlib: A library for creating static, animated, and interactive visualizations in Python


max_depth: In tree-based models, a hyperparameter that controls how deep each base learner tree will grow

max_features: In decision tree and random forest models, a hyperparameter that specifies the number of features that each tree randomly selects during training called “colsample_bytree” in XGBoost Maximum Likelihood Estimation (MLE): A technique for estimating the beta parameters that maximizes the likelihood of the model producing the observed data Mean: The average value in a dataset
Measure of central tendency: A value that represents the center of a dataset
Measure of dispersion: A value that represents the spread of a dataset, or the amount of variation in data points
Measure of position: A method by which the position of a value in relation to other values in a dataset is determined
Measures: Numeric values that can be aggregated or placed in calculations
Median: The middle value in a dataset
Mentor: Someone who shares knowledge, skills, and experience to help another grow both professionally and personally
merge(): A pandas function that joins two dataframes together; it only combines data by extending along axis one horizontally


Merging: A method to combine two (or more) different dataframes along a specified starting column(s)
Method: A function that belongs to a class and typically performs an action or operation
Metrics: Methods and criteria used to evaluate data min_child_weight: In XGBoost models, a hyperparameter indicating that a tree will not split a node if it results in any child node with less weight than this value called “min_samples_leaf” in decision tree and random forest models
min_samples: In DBSCAN clustering models, a hyperparameter that specifies the number of samples in an ε-neighborhood for a point to be considered a core point (including itself)