Machine learning algorithms can be incredibly complicated. They often take months of work and many pages of textbook reading in order to understand. These algorithms utilize statistical analysis to produce reams of output that can change substantially over time. However, not all machine learning algorithms are this complicated. Some are derived from more basic tools used to categorize information and make predictions based off of that information. One example of this more simplistic approach is the decision trees algorithm. Decision trees can be helpful in visualizing information, making connections, and greatly easing the process of making predictions over numerous steps.
How Does Decision Tree Algorithm Work
Decision trees are one of the more basic algorithms used today. At its heart, a decision tree is a branch reflecting the different decisions that could be made at any particular time. The process begins with a single event. Then, a “test” is performed in the event that has multiple outcomes. Those outcomes are mapped onto a tree and comprise the tree’s branches. Each branch can then have the same test applied or another test. This test produces another series of branches which can then continue to fan out over an unlimited number of tests. Eventually, the decision tree is a massive set of jumbled numbers and outcomes. Branches can be improved over their original appearance.
Numerous attributes can be added to the decision tree. One of these is the probability of each decision occurring. Few events have multiple probabilities that are all equal. There can be an added number that displays the percentage of each event occurring. This process greatly aids with decision-making in a complicated decision tree. Even an inexperienced observer can add up all of the percentages attached to a path on a decision tree and figure out the percentage of a tree following a path to a particular outcome.
What are Decision Tree Nodes?
Another addition to standard decision trees is the concept of nodes. There are three types of nodes that help make sense of the decisions being made. One of these is the decision node. These nodes are represented by small squares and they represent multiple certain outcomes. Chance nodes are represented by squares and are less certain. They branch off into a group of possibilities with different probabilities that do not always add up to 100%. Finally, there is the end node. The end node is represented by a triangle and is located at the end of decision-making. It is the eventual result that all of the previous decisions are striving towards.
Different Types of Decision Trees Algorithms
A machine learning algorithm helps to make sense of decision trees and their many branches. These algorithms work from either a supervised or an unsupervised set. In a supervised setting, there is an example set that the machine learning algorithm is attempting to replicate. A decision trees algorithm used for prediction would use an eventual outcome as its example set. The algorithm then creates tests and decision trees attempting to replicate that eventual outcome.
It produces an output and then compares that output to the original example set. Learning occurs with the weights and percentages distributed throughout the decision tree. The machine learning algorithm changes these weights depending on the performance of the original tests and tries to perfect the system towards the example set. This process occurs thousands of times and results in a machine learning algorithm that is more responsive and accurate over time.
An unsupervised set works somewhat differently. There is no example set for the decision trees algorithm to work towards. Therefore, the algorithm simply performs its function according to a series of guidelines. It produces categories or makes predictions based off of the percentages of the different branches of the decision tree. The system then shifts over time depending on the outputs produced by the algorithm and how well the algorithm meets its categorizing and predictive objectives. This makes the algorithm freer to learn and develop according to its own results and not an example set predetermined by people or other algorithms.
Decision Trees Algorithm Implementation
Decision trees have a variety of uses in many different fields. They can be used to help an operator or a computer make a decision. Those decisions can be broken down and analyzed repeatedly in a short period of time. Decision trees may be used in a program that finds the solution with the highest probability and then pursues it. This tool can be used for sorting and analyzing data. Decision trees help identify trends and categories.
A category can be created for every possible decision with a probability of over or under a certain percentage. These categories can then be used to sort massive amounts of information over time. Decision trees may also be used to pare down data. Outliers can be identified as decisions that only have a small or extremely large probability of being chosen. Such paring down ensures that a data set more accurately reflects the information that the algorithm is attempting to find out.
Decision Trees Algorithm Potential
The potentials of the decision trees algorithm are vast. They can put large quantities of information to work by sorting categories and making predictions. However, the more impressive quality of decision trees is their simplicity. Simple branches and shapes can visualize countless decisions and hours of processing by computers. The ability to visualize can help more people understand how a machine learning algorithm and a data mining example work through the creation and display of decision trees. This quality makes the decision trees algorithm an indispensable part of any machine learning operation.