How to Install Python and the Machine-Learning Module for Predictive Analysis
How to Create an Unsupervised Learning Model with DBSCAN
Basics of Classification Models for Analytical Predictions

How to Visualize Your Model's Analytical Results: Decision Trees and Predictions

When you present the results of your predictive analysis to your stakeholders, creating visual representations of the data can help them to easily understand the information and make well-informed decisions. Here are two methods of visual representations that you can use:

How to visualize Decision Trees

Many models use decision trees as their outputs: These diagrams show the possible results from alternative courses of action, laid out like the branches of a tree. Here is an example of a decision tree used as a classifier: It classifies baseball fans based on a few criteria, mainly the amount spent on tickets and the purchase dates.

From this visualization, you can predict the type of fan that a new ticket-buyer will be: casual, loyal, bandwagon, diehard, or some other type. Attributes of each fan are mentioned at each level in the tree (total number of attended games, total amount spent, season); you can follow a path from a particular "root" to a specific "leaf" on the tree, where you hit one of the fan classes (c1, c2, c3, c4, c5).

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Suppose you want to determine the type of baseball fan a customer is so that you can determine what type of marketing ads to send to the customer. You want to know whether the customer is a baseball fanatic or someone who just rides the bandwagon.

Suppose you hypothesize that baseball fanatics and bandwagon fans can be persuaded to buy a new car (or other discretionary goods) when their team is doing well and headed for the playoffs. You may want to send them marketing ads and discounts to persuade them to make the purchase.

Further, suppose you hypothesize that bandwagon fans can be persuaded to vote in support of certain political issues. You can send them marketing ads asking them for that support. If you know what type of fan base you have, using decision trees can help you decide how to approach it as a range of customer types.

How to visualize predictions

Assume you've run an array of predictive analytics models, including decision trees, random forests, and flocking algorithms. You can combine all those results and present a consistent narrative that they all support. Here confidence is a numerical percentage that can be calculated using a mathematical function.

The result of the calculation encapsulates a score of how probable a possible occurrence is. On the x axis, the supporting evidence represents the content source that was analyzed with content-analytics models that identified the possible outcomes.

In most cases, your predictive model would have processed a large dataset, using data from various sources, to derive those possible outcomes. Thus you need show only the most important supporting evidence in your visualization.

A summary of the results obtained from applying predictive analytics is presented as a visualization that illustrates possible outcomes, along with a confidence score and supporting evidence for each one. Three possible scenarios are shown:

  • The inventory of Item A will not keep up with demand if you don't ship at least 100 units weekly to Store S. (Confidence score: 98 percent.)

  • The number of sales will increase by 40 percent if you increase the production of Item A by at least 56 percent. (Confidence score: 83 percent.)

  • A marketing campaign in California will increase sales of Items A and D but not Item K. (Confidence score: 72 percent.)

The confidence score represents the likelihood that each scenario will happen, according to your predictive analytics model. Note that they are listed here in descending order of likelihood.

Here the most important supporting evidence consists of how excerpts from several content sources are presented over the x axis. You can refer to them if you need to explain how you got to a particular possible scenario — and trot out the evidence that supports it.

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The power behind this visualization is its simplicity. Imagine, after months of applying predictive analytics to your data, working your way through several iterations, that you walk into a meeting with decision-maker. You're armed with one slide visualization of three possible scenarios that might have a huge impact on the business. Such a visualization creates effective discussions and can lead management to "aha" moments.

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