Choosing the Right Chart for Your Debrief

The type of chart available depends on whether it is appropriate for visualizing the type of influencer analysis in your debrief. Depending on how you are visualizing your model data, you can choose from the following chart types:

Chart type	Use to	Example
Pie	Compare categorical data as percentages. If you have more than 10 contributing influencers, then using a bar or column chart would show a clearer view of the data spread.	Show the predicted percentage breakdown of contributing voting regions to the results of a national election.
Bar	Compare categorical data along the vertical axis by the category count or percentage on the horizontal axis displayed as bars.	You have an employee turnover predictive model that predicts potential churn level for staff. Your target variable is Employee Churn Estimate. The influencers Marital-status, Age, Qualification Level, Salary, Recent Promotion, and Training Participation are plotted along one axis, and the percentage contribution of each category to Churn Estimate is plotted as a bar or column on the other. For a table, the columns would be the category influencers Marital-status, Age, Qualification Level, Salary, Recent Promotion, and Training Participation, and the percentage contribution of each category to Employee Churn Estimate appears in each cell row.
Column	Show the same information as a bar chart, with the axes interchanged: inlfuencers along the horizontal axis by the group count, or percentage values on the vertical axis displayed as columns.
Table	Represent the same type of information as column and bar charts, but in a table format where the categorical inlfuencers are represented as columns, with the count or percentage values in row cells.
Radar	Display data for multiple influencers in two dimensions with multiple categories represented on radial axes.	You have a predictive model to predict sales of candy. Your target variable is Chocolate Sales, and you plot different chocolate flavors around the radial axes of a bubble chart. Your categorical influencer that is measured over the axes is three brands of chocolate. The spread of sales figures around the axes would give a good idea of which different brands would do better for the same flavor than others.
Tag Cloud	Represent category influencer names as text juxtaposed geographically on a canvas, where the font size of each text label indicates the influence on the target variable. Tag charts are useful when the influencer names have semantic significance, for example keywords in a twitter feed, country names, business companies' stock market values, or different television shows' audience ratings for a night's viewing.	A retail chain selling multimedia and cultural products wants to venture into publishing to produce a compilation of "retro" styled detective stories. The target audience is younger readers not familiar with traditional detective characters. They develop a predictivve model including influencers such as education level, age, buying history for DVDs, books, games, MP3s and streaming video, to predict a possible taste for different detective profiles. The results could be easily represented as a tag cloud with the names most likely or not to appeal, for example Sherlock Holmes, Father Brown, Miss Marple, Hercule Poirot, Auguste Dupin, Philip Marlowe, and others.
Line	Show a model performance curve.	You want to see the performance curve of your training predictive model compared to the validation and random plots for a predictive model that predicts what percentage of a population are identified positively as having a disease, after being tested using a new screening test.
Bubble	See the correlation between two influencers, one dependent on the other. The correlation is represented by third influencer at the plot position and the area of the plot shows the magnitude of the relationship.	You have a predictive model to predict fatal car accidents. Using a bubble chart, you could evaluate dependency between influencers such as "Car Accident Frequency" and "Speed", with a categorical influencer of Yes or No for Fatality.