Flight delay classification - Logistic Regression and Two class Boosted tree
Predict whether or not a flight will be delayed based on airports and weather. Can be built and run in a guest account.
In this experiment, we use historical on-time performance and weather data to predict whether the arrival of a scheduled passenger flight will be delayed by more than 15 minutes. We approach this problem as a classification problem, predicting two classes -- whether the flight will be delayed, or whether it will be on time. Broadly speaking, in machine learning and statistics, classification is the task of identifying the class or category to which a new observation belongs, on the basis of a training set of data containing observations with known categories. Classification is generally a supervised learning problem. Since this is a binary classification task, there are only two classes. To solve this categorization problem, we will build an experiment using Azure ML Studio. In the experiment, we train a model using a large number of examples from historic flight data, along with an outcome measure that indicates the appropriate category or class for each example. The two classes are labeled 1 if a flight was delayed, and labeled 0 if the flight was on time.