A Brief Note On Random Forest Tree Based Approach It Uses Gain And Variance For Prediction

Naïve Bayes is a classifier based on Bayes theorem, this classifier assumes that the result of an attribute value on a class is independent of the other attributes values.

AdaBoost and C4.5 decision tree. The first classifier used is Naïve Bayes it is a probabilistic classifier

Pre-processing: To build a decision trees model we discretize all the numeric attributes using the Filters options. We use 4 bins to discretize all numeric attributes. We leave the class attribute (Quality) as type numeric. By using the default parameters for J48, we get the model shown in Fig-2.

Also I am recommending this tool, because the decision tree analysis process, which are of great importance in the industrial production of high quantity standardized products (Shoes, sandals, etc) and lately has gained importance in the low volume production of customized products. Due to the high capital requirements when looking for the opening of a new store, decision tree analysis is of great relevance for the manufacturing business. Because of it, this attracted the attention of the manufacturing industry, who tried to support practical cost-effectively analysis by using the proper profitability models.

For the second week, December 10 – 16, the important variables were CY48, VaccinationRate, AverIncome, WinterAfterHumidity. The best model was also a decision tree. In this week, the training misclassification rate was 0.3158 and the validation miscalculation rate was 0.4651. The difference in the rates is noticeable and I would declare this model as useless. The model fell apart during the validation process and I would not trust it prediction future events.

Decision Trees are useful tools for helping you to choose between several courses of action.

from statistics, machine learning, and visualization. The most commonly used methods are Decision Trees, Neural Networks, Genetic Algorithms, and Rough Set Analysis (Hajizadeh, et al., 2010). Due to prediction and

it is the most effective data mining technique to discover hidden or desired pattern among the large amount of data. It is responsible to find correlation relationships among different data attributes in a large set of items in a database .

(Gong, Wang, and Peng 2016) [3] The data mining process is enhanced by the use of bijective soft set tool. This tool helps in mining the soft sets. The recognition ability is lost by bijective data set if faulty data is presented. So it is utmost important to handle faulty data. For this purpose β misclassification degrees is defined.

Data Mining is the non-trivial extraction of potentially useful information about data. In other words, Data Mining extracts the knowledge or interesting information from large set of structured data that are from different sources. There are various research domains in data mining specifically text mining, web mining, image mining, sequence mining, process mining, graph mining, etc. Data mining applications are used in a range of areas such as it is used for financial data analysis, retail and telecommunication industries, banking, health care and medicine. In health care, the data mining is mainly used for disease prediction. In data mining, there are several techniques have been developed and used for predicting the diseases

There are plenty of data mining tools available in the market and most of the seller also provides the demo or freeware version. Some of them are RapidMiner, WEKA, R-Programming, Orange, KNIME, NLTK etc (http://thenewstack.io/six-of-the-best-open-source-data-mining-tools/)

Data Mining is the non-trivial extraction of potentially useful information about data. In other words, Data Mining extracts the knowledge or interesting information from large set of structured data that are from different sources. There are various research domains in data mining specifically text mining, web mining, image mining, sequence mining, process mining, graph mining, etc. Data mining applications are used in a range of areas such as it is used for financial data analysis, retail and telecommunication industries, banking, health care and medicine. In health care, the data mining is mainly used for disease prediction. In data mining, there are several techniques have been developed and used for predicting the diseases that includes data preprocessing, classification, clustering, association rules and sequential patterns. This paper analyses the performance of two classification techniques such as Bayesian and Lazy classifiers for hepatitis dataset. In Bayesian classifier there are two algorithms namely BayesNet and NaiveBayes. In Lazy classifier we have two algorithms namely IBK and KStar. Comparative analysis is done by using the WEKA tool.It is open source software which consists of the collection of machine learning algorithms for data mining tasks.

Thus a multi class classification of SVM is choosen here for the classification from Cody Neuburger [18]. In traditional SVM; the structure of trained SVM is formed in a 1×1 structure. And from that structure

2. Classification stage – applying the algorithm on the dataset to get FDT (Fuzzy Decision Tree) and analyse them to get results.

Data is very critical for any organization, industry or business process. Data which was in gigabytes or terabytes