Decision Tree Analysis On Decision Trees

We were assigned to construct a software that utilizes a classification algorithm that is able to accurately decide a correct classification for a certain sequence of inputs that were provided by the user. The input is to be classified based on a known training set of records of the same attributes as the sequence provided by the user.

After the end of WWI the roaring 20’s came to life, the economy was booming, and the U.S. felt a sense of pride. Sadly though all was not as grand as it seemed the land was stained with blood from racial issues. In the book The Learning Tree by Gordon Parks a coming to age story Newt Winger tells the tale of “how it feels to be black in the white man’s world.” He faces the struggle of the racial issue as he grows up in his little town of Cherokee Flats. The young twelve year old lives in a society where blacks are slowly being integrated but are still left with the shortest straw; not being allowed to eat or work in some places and the high schools black students are not allowed on the football nor the baseball teams while a certain teachers

In this module, the class label for the testing data is predicted. The n – dimensional feature vector for the testing data is converted from query tree of testing data in the manner similar to the data pre – processing phase. The SQLIA classifier determines the new testing feature vector is normal or malicious, by using optimized SVM classification model.

A decision tree is a diagram consisting of circles decision nodes, square probability nodes and branches.

E) Decision trees are solved by starting at the first decision node and moving forward.

Marcel Proust quotes “the real voyage of discovery consists not in seeking new landscapes, but in having new eyes”. In this manner Discoveries not only allow for a surface level form of exploration but can however affirm or challenge individuals assumptions and beliefs about the world. Invictus by William Henley and The Red Tree by Shaun Tan explore the process of transformation within a Discovery, which is a catalyst to which responders are able to reaffirm or challenge their holistic beliefs about the world.

4. A decision tree is a diagram consisting of circles decision nodes, square probability nodes, and branches.

Nowadays, in the education industry has been a highly competitive industry and many new competitors are entering the market. Learning Tree International, Inc. was originated in 1974 and headquartered in Reston, Virginia, it is considered one of the well-known companies in the education industry. According to Yahoo Finance, Learning Tree Inc. has 393 full-time employees. Learning Tree International, Inc. (LTRE), operates in the education and training services industry (SIC code: 8200). The services that the company provides are training and education for commercial and government information technology and management professionals. Also, it known for its spread worldwide and that they offer their services online through what they call “Learning

Data mining is another concept closely associated with large databases such as clinical data repositories and data warehouses. However data mining like several other IT concepts means different things to different people. Health care application vendors may use the term data mining when referring to the user interface of the data warehouse or data repository. They may refer to the ability to drill down into data as data mining for example. However more precisely used data mining refers to a sophisticated analysis tool that automatically dis­ covers patterns among data in a data store. Data mining is an advanced form of decision support. Unlike passive query tools the data mining analysis tool does not require the user to pose individual specific questions to the database. Instead this tool is programmed to look for and extract patterns, trends and rules. True data mining is currently used in the business community for market­ ing and predictive analysis (Stair & Reynolds, 2012). This analytical data mining is however not currently widespread in the health care community.

The tree diagram is a graphic representation of the tree model. This tree diagram shows that:

Today at Learning Tree, it was brought to my attention that Amiah and another little girl named Isabella (Amiah told me her name and that she was also in the same class), I believe it is, have been exchanging minor verbal altercations that were witnessed and dealt with by the classroom staff. However, it became an issue today when Isabella told her parents Amiah had been bullying her. The staff member informed me of this and that on the contrary of what the child said; it was more mutual words being exchanged with the other child initiating the conversation. I just wanted to inform you on what was going on and that I have instructed Amiah and her Learning Tree teacher agreed for the afternoon program to move away from her at all times to cut

How data mining can assist bankers in enhancing their businesses is illustrated in this example. Records include information such as age, sex, marital status, occupation, number of children, and etc. of the bank?s customers over the years are used in the mining process. First, an algorithm is used to identify characteristics that distinguish customers who took out a particular kind of loan from those who did not. Eventually, it develops ?rules? by which it can identify customers who are likely to be good candidates for such a loan. These rules are then used to identify such customers on the remainder of the database. Next, another algorithm is used to sort the database into cluster or groups of people with many similar attributes, with the hope that these might reveal interesting and unusual patterns. Finally, the patterns revealed by these clusters are then interpreted by the data miners, in collaboration with bank personnel.4

Data classification is the process of organizing data into categories for the most effective and efficient use. A well-constructed data classification system is a staple of any data loss prevention policy because it

Classification tree is a model that uses both categorical and numeric inputs to predict categorical or binomial outputs. The software draws a graph composed of nodes and leaves representing different groups of data with same characteristics based on the model. The output looks like a tree, which provides viewers with a direct exhibition; therefore, it is a good tool we can use to make a statistical analysis. The classification tree model allows both numeric and categorical inputs, both of which appear in the data set, that can be utilized to predict our categorical targeted output: workday alcohol consumption level. The model’s ability to handle both numeric and categorical input was the main reason why we decided that classification trees would be a good model to analyze our data. One of the most important changes to our data set, mentioned previously, was converting the workday alcohol consumption column into two levels: zeros, representing low workday alcohol consumption, and ones, representing high workday alcohol consumption. Moreover, since this data set contains the results of over one thousand questionnaire surveys, often times numbers recorded in the data set have more meaning than the number they represent. Take the “Health” variable as an example. If a respondent recorded a one for their health, it meant that their health was extremely poor; therefore, in this case one does not represent the actual number but rather extremely poor.

There are growing researches in data mining as a part of education. This new developing field, called Educational Data Mining, concerns with creating techniques that find information from data originate from educational situations. The data can be collected structure verifiable and operational data dwell in the databases of educational establishments. The understudy data can be close to home or scholastic. Additionally it can be gathered from e-learning frameworks which have a vast measure of data utilized by mostly organizations. Educational data mining utilized numerous strategies, for example, decision trees, neural systems, k-nearest Neighbor, Naive Bayes, help vector machines and numerous