The Decision Tree Algorithms And Grows The Tree By Recursive Partitioning

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

The right branch has records 1,3,8,9,10. Now we split the right child which has records 1,3,8,9,10. Candidate Split Left Child Node, tL Right Child Node, tR

I believe that the book should be written from the perspective of the truffula trees because it shows how exactly the trees represent plants and animals that suffering due to the human impact on the environment. My perspective shows how business and industries are taking over animals’ homes and food sources. Animals become extinct because because humans hunt them, pollution, or the destruction of the places where they live.  It also shows how forests are being cut down to make business and to use the wood to make buildings.lastly, all the chemicals in the air affect our environment in a bad way causing air pollution which hurts people and animals. All in all, i think that the book should be written in the truffula trees’ perspective because

In this experiment, we found that the performance of random forests is optimal with around 70% accuracy on publicly available “Diabetes 130-US hospitals for year 1999-2008” dataset. The proposed model also indicates that information regarding diagnosis, age, race, medications, admission types, lab procedures are highly influential for readmission classification. In this work, the result is evaluated in different criteria. The best AUC and f-measure values obtained on the experiment dataset were 70.1% and 66.6% the classification of the readmitted patients from non-readmitted patients using random forests. It can be observed Random Forests is performed optimal than Naïve Bayes and C4.5.

This system is divided in a knowledge base and inference rules. In the video, this system is used when it comes the control center. The employees are checking flights and regulating flows thanks to precious information given in their computer like the weather, the flow rate and a packaging monitoring.

Question 1: Assume a base cuboid of 10 dimensions contains only three base cells: (1) (a1, b2, c3, d4; ..., d9, d10), (2) (a1, c2, b3, d4, ..., d9, d10), and (3) (b1, c2, b3, d4, ..., d9, d10), where a_i != b_i, b_i != c_i, etc. The measure of the cube is count. 1, How many nonempty cuboids will a full data cube contain? Answer: 210 = 1024 2, How many nonempty aggregate (i.e., non-base) cells will a full cube contain? Answer: There will be 3 ∗ 210 − 6 ∗ 27 − 3 = 2301 nonempty aggregate cells in the full cube. The number of cells overlapping twice is 27 while the number of cells overlapping once is 4 ∗ 27 . So the final calculation is 3 ∗ 210 − 2 ∗ 27 − 1 ∗ 4 ∗ 27 − 3, which yields the result. 3, How many

Preparing a data set for analysis in data mining is a more time consuming task. For preparing a data set it requires more complex SQL quires, joining tables and aggregating columns. Existing SQL aggregations have some limitations to prepare data sets because they return one column per aggregated group. In general, significant manual efforts are required to build data sets, where a horizontal layout is required. Also many data mining applications deal with privacy for many sensitive data. Therefore we need privacy preserving algorithm for preserving sensitive data in data mining. Horizontal database aggregation is a task that involves many participating entities. However, privacy preserving during such database aggregation is a challenging task. Regular encryption cannot be used in such cases as they do not perform mathematical operations & preserve properties of encrypted data. This paper has two main approaches preparing Data set and privacy preserving in data mining. For preparing the data set we can use the case, pivot and SPJ method for preparing the horizontal aggregation and then employ a homomorphic encryption based scheme for data privacy during aggregations. Homomorphic encryption is the conversion of data into cipher text which allows specific types of computation operations to be performed on the data set and obtains encrypted result. The encrypted result is same as the result which is performed on the plain text. Although such schemes are already being used for

Abstract: In this term paper study discuss about the important and aim of the study in this article assist to know how managing electronic records at public agencies in Malaysia using function-based classification. Electronic records ensures that the information of organization has the records it needs when they are needed. It is information captured through electronic means, and which may or may not have a paper record to back it up. According in this article, the Department of Syariah Judiciary Malaysia is the first investigation case that serve the function to make the snapshot that can be used at certain time for the future. The process of classifying records for retrieval, appraisal and disposal more easily when used the function based classification model. Development of function based records classification from context level to decomposed diagram using IDEF0 tool by experts academically and professionally. The case study was taken by Malaysian legal records. Function-based classification that show the context for records rather than content other than support proactive management of records.

Divide-and-conquer, breaks a problem into subproblems that are similar to the original problem, recursively solves the subproblems, and finally combines the solutions to the subproblems to solve the original problem. You should think of a divide-and-conquer algorithm as having three parts:

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.

Abstract: Lot of research done in mining software repository. In this paper we discussed about the static model of data mining to extract defect .Different algorithms are used to find defects like naïve bayes algorithm, Neural Network, Decision tree. But Naïve Bayes algorithm has the best result .Data mining approach are used to predict defects in software .We used NASA dataset namely, Data rive. Software metrics are also used to find defects.

Network of nodes based on Naïve Bayes classifier is termed as Bayesian Net. This can be applied to larger datasets as compared to Naïve Bayes.

2.4 Support Vector Machine (SVM) The support vector machines are supervised learning models, derived from statistical learning theory (Vapnik 1995) that analyze data and recognize pattern. SVM effectively perform non-linear classification by using kernel functions, implicitly mapping their inputs into high-dimensional spaces. This makes it a suitable tool in predicting the compressive strength of concrete which is non-linearly related to its mix ingredients.

Support Vector Machine (SVM) is primarily a classier method that performs classification tasks by constructing hyperplanes in a multidimensional space that separates cases of different class labels. SVM supports both regression and classification tasks and can handle multiple continuous and categorical variables. For categorical variables a dummy variable is created with case values as either 0 or 1. Thus, a categorical dependent variable consisting of three levels, say (A, B, C), is represented by a set of three dummy variables:

Abstract—The field of information extraction and retrieval has grown exponentially in the last decade. Sentiment analysis is a task in which you identify the polarity of given text using text processing and classification. There are various approaches in the task of classification of text into various classes. Use of particular algorithms depends on the kind of input provided. Analyzing and understanding when to use which algorithm is an important aspect and can help in improving accuracy of results.