Spatial data, also known as “Geospatial Data” refers to records in the dataset that have a geographic aspect. It can be the location of a store, the distance between two bus stops, or the boundaries of a state. Alteryx offers the spatial toolset to help users derive meaningful insights and information from spatial data.  

Alteryx has built-in Decision Trees capabilities. These are some of the easiest models to understand and illustrate as they are straightforward and don't require complex interpretation. Decision Tree models are good for handling complex, non-linear relationships, and outliers on the data, but it also has its disadvantages. Some of its predictions tend to be weak since it is prone to overfitting. They are not very robust, because small changes in the training data can give a larger change in the output. However, they are one of the most commonly used analytical models around. 

Logistic Regression is a binary classification algorithm. Meaning, it predicts either true or false, churn or not, pass or fail and other binary categorical values. This algorithm also uses a regression function which classifies the target variable based on probability. Logistic regression fits the data to an “S” shaped logistic function also known as the Sigmoid Curve. It curves from 0 to 1, where 0 is the least probable or false, while 1 is the most probable or True. This splits the data into one of the two categories and each row of data is classified by comparing the probability to a threshold between categories. With Alteryx, it's included.