Decision Trees
Decision trees are a tool that models a series of decisions and the possible consequences. They typically are used in the fields of information theory, operations research, and operations management, but are also used in machine learning as a supervised learning algorithm. Typically, decision trees are made up of nodes, where at each node, depending on the current state, a decision is made as to whether to go down one subtree or another. At the end of a decision tree, the leaf nodes contain a conclusion about the data. In the field of machine learning, that is typically a prediction about the input based on the results of previous inputs. In supervised machine learning, decision trees are popular due to their simplicity and similarity to a human approach.
In this research into the effect of Covid-19 on food security, decision trees were created for the search data from the Google API and the survey data provided by the US census. For the Google Search API data, the goal was to attempt to better understand the most important features of the dataset when it came to predicting the type of article. This was similar to clustering, but by using decision trees, it is much easier to see what words matter. For the US Census data, the goal was to try to predict the effect of Covid-19 on food security and its effect on different breakdowns of society. These breakdowns included race, gender, and age group.
Search Data Decision Trees
The first set of data used with decision trees is the search data gathered and cleaned in previous steps. This data comes from two different sources and is combined here into one. The data comes from the Google Search API, where 4 different searches found 20+ articles each concerning the effect of different disasters on food security. In parallel, a set of research papers on the same topics were added to the mix to give a better set of predictions. Using this set of data, decision trees are used to try to get a better understanding of how each disaster affects food security. Click on the link below to find out more.
More on predicting search data
Survey Data Decision Trees
The second set of data used with decision trees is the survey data gathered and cleaned in the previous steps. This data set comes from two different sources and is cleaned and combined into one for further processing. The data comes from the US Census and another 3rd data source that collects information on US lockdowns. Using this data, decision trees are used to try to predict a breakdown in society, specifically race, gender, and age group. Click on the link below to find out more.