Artificial Intelligence

 Association Rules |Clustering |Decision Trees| Naive Bayes| Regression Analysis| Neural Networks| Time Series |Text Mining |

Genetic Programming

Decision Trees help in taking decisions fast from various options and particularly find applications in making Business Decisions having various options open at a particular time interval. Algorithms automatically create splits and create more tree branches. Decision trees are excellent tools for making financial or number based decisions where a lot of complex information needs to be taken into account. They provide an effective structure in which alternative decisions and the implications of taking those decisions can be laid down and evaluated.

Decision nodes
Three popular rules are applied in the automatic creation of classification trees. The Gini rule splits off a single group of as large a size as possible, whereas the entropy and twoing rules find multiple groups comprising as close to half the samples as possible. Both algorithms proceed recursively down the tree until stopping criteria are met.


Advantages
Amongst decision support tools, decision trees (and influence diagrams) have several advantages:

Decision trees:

These are simple to understand and interpret. People are able to understand decision tree models after a brief explanation.
have value even with little hard data. Important insights can be generated based on experts describing a situation (its alternatives, probabilities, and costs) and their preferences for outcomes.
 

 If a given result is provided by a model, the explanation for the result is easily replicated by simple math can be combined with other decision techniques.

Predicting future outcomes and identifying factors that can produce a desired effect are often the main goals of data analysis and data mining. Decision trees are one of the most popular methods of predictive modeling for data mining purposes because they provide interpretable rules and logic statements that enable more intelligent decision making.

A decision tree partitions data into smaller segments called terminal nodes or leaves that are homogeneous with respect to a target variable. Partitions are defined in terms of other variables called input variables, thereby defining a predictive relationship between the inputs and the target. This partitioning continues until the subsets cannot be partitioned any further using user-defined stopping criteria. By creating homogeneous groups, analysts can predict with greater certainty how individuals in each group will behave. For example, in database marketing, decision trees can be used to segment groups of customers and develop customer profiles to help marketers produce targeted promotions that achieve higher response rates.