AroniSmartIntelligence™: Optimized Machine Learning, Advanced Analytics, and Data Science Capabilities; A New Capability With Decision Trees

AroniSmartIntelligence™: Decision Tree Module

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AroniSmartIntelligence ™ is one of the most advanced analytics tools with Machine Leaning, Data Science, Statistics, Econometrics,  NLP Text and Sentiment analysis, Neural Network, and Support Vector Machine capabilities. To support NLP Text and Sentiment analysis, a new capability has been recently added: Speech-to-Text Transcription. Now the AroniSmartIntelligence version will also include a critical capability: Decision Trees, complementing the usual Bayesian Network  and Regression classification tools. 

Decision Trees will help solve the various problems related to classification built on big data and fuel data science insights. 

AroniSmartIntelligence ™, available on Apple's App Store®,  is intended, very useful, and handy for experts, professionals,  and college and graduate students in Statistics, Machine Learning, Quantitative Business Analytics, Data Science, Econometrics, STEM, Operations Research, and other related fields. It includes a detailed Handbook and a well crafted and instructive User's Manual. It is a cutting-edge analytical tool for advanced analytics and developing actionable insights without the hassle of coding. For more visit here AroniSmartIntelligence™: Optimized Machine Learning, Advanced Analytics, and Data Science Capabilities Including Econometrics, Bayesian Models, Neural Network Models, Marketing Mix Models, and NLP Analytics  

 AroniSmartIntelligence™  Decision Trees Capability for Data Mining and Data Science.

Machine Learning and Data Science Models are fastly improving and becoming the center of advanced analytics, business intelligence, decision making, management science, data science, and other areas in which sound informed decisions are needed.  

 Decision Trees algorithms have been some of the most frequently used capabilities to address different classification problems.  Decision Trees have been critical in Data Mining or Data Science.  The basic idea of Data mining and Data Science techniques is summarizing and analyzing the data from different directions and dimensions and generate relevant insightts.

Recently data has been  increasing exponentially, and Data Science and  Data Mining have been adjusting to deal with such a huge amount of data needs.  With the recent critical advances in modeling, an advanced capability on Natural Language Processing (NLP) and Other Text Analytics was added, to address the speech-to-word translation (see here: AroniSmartIntelligence™: Optimized Machine Learning, Advanced Analytics, and Data Science Capabilities; A New Capability With Speech-to-Text Transcription ).

Unlike NLP and  Speech-To-Text Transcription models, the data analysis techniques of Decision trees are tools for  sorting large datasets into meaningful forms  and provide insights to understand the relationships among the key attributes  or variables, improve understanding of the connections  and  use the analytical insights to  solve often complex problems of data cleansing, clustering, classification, and prediction. 

Hence, AroniSmart™ team has been improving and optimizing the data mining and data science techniques to  organize, search, analyze,  order, and  find the meaningful patterns within the data in order to generate actionable insights.

 AroniSmartIntelligence™ Decision Tree Algorithms.

The data generated by billions and billions of sources today come in all types of formats.  AroniSmartIntelligence™ tools already include several algorithms, capabilities and models to address the critical  needs.

The Machine Learning, Data Mining capabilities of AroniSmartIntelligence™ have included a number of algorithms  used in the  classification  and decision. These include the Bayesian Models, Neural Network, Regression models, and Big Data. Among the examples of algorithms and models in AroniSmart tools are   Bayesian Models  (Bayes Net, Naïve Bayes, Naïve Bayes Multinomial, Bayes Multinomial Updatable, Naïve Bayes and Naïve Bayes Simple ) ,  Neural Network (Multilayer Perceptron) and Regression  functions (Gaussian Processes, Linear Regression, Logistic,  SGD, SGD Text, Simple Linear Regression, Simple Logistic, SMO, SMO reg and Voted Perceptron),

For Decision Trees, AroniSmartintelligence™ will include 5 types of Trees, mostly used to solve the current classification and decision problems with the highest accuracy. The Decision Trees in AronismartIntelligence™ are:

  • Decision Stump;
  • Hoeffding tree;
  • Random Forest;
  • Random Tree;
  • REP Tree.

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AroniSmartIntelligence, the leading tool for Advanced Analytics, Machine Learning & Data Science

Statisticians, Data Scientists, Business and Financial Analysts,  Savvy Investors, Engineers, Researchers,  Students, Teachers, Economists, Political Analysts,  and most of  the practitioners use Advanced Analytics to answer questions, to support informed  decision making or to learn.

AroniSmartIntelligence™ is a leading Advanced Analytics, Machine Learning and Data Science tool, with optimized cutting edge Statistics models, Econometrics, Big Data and Text Analytics.

AronismartIntelligence™ includes modules covering Machine Learning,  Big Data mining, Bayesian Statistics, Neural Network Models, Unstructured Text Analysis, Sentiment and Emotion Analytics,  and other advanced analytics.


AroniSmartIntelligence™  Decision Trees capability implementation.

Decision Treescapability implemented in AroniSmartIntelligence™ focused on addressing the need to  find meaningful patterns and actionable insights within the data, given the recent exponentially increasing  trends in data availability.  

Decision Trees in AroniSmartIntelligence are part of the supervised learning.   Some of the key AroniSmart™ capabilities address the problems related to unsupervised learning.  In fact, the Segmentation  and Big Data module of AroniSmartIntelligence focus on the unsupervised learning and clustering.

AroniSmartIntelligence™ helps in addressing the problem of Classification, a supervised learning in which the labels or values of the class (or dependent variable) attributes, are determined and  known in advance. This is different from the clustering, which is unsupervised learning due to unknown labels for classes.  Hence,  AroniSmartIntelligence™  modules of Machine Learning, Statistics Econometrics,  Data Science and  Data Mining problem address the issues of classification and help to select features and build preidictive models.

The models are designed to leverage two types of data, ceated using several proprietary algorithms.   The models are first built on a selected or created  training data set. Then,  model evaluations and the  predictions  are built using the a testing data.  Once the models are trained and validated , they are  ready to be used on a new set of data to support informed decisions and activation (see AroniSmartIntelligence™: Marketing Mix and Multitouch Attribution Models)

Insights on the Decision Tree Models in AroniSmartIntelligence™.

Each of the 5 types of Decision Tree  in AroniSmartintelligence™  has its own benefits and requirements. However, all the trees are  used to solve the current classification and decision problems to improve and get the highest accuracy. 

Decision Stump

 Decision Stump Tree leverages  a regression or classification, using the boosting algorithm. Usually used in conjunction with a boosting algorithm. Does regression (based on mean-squared error) or classification (based on entropy). Missing class labels and values are treated as separate values.

 Hoeffding tree:

 Hoeffding Tree  is an incremental decision tree used for learning from massive data streams, assuming that the distribution generating examples does not change over time.  Hence, the small sample are assumed to represent the overall picture and hence cane be used  to choose an optimal splitting attribute. 

Random Tree:

Random Tree is used to build a tree using a number of randomly chosen attributes at each node. It allows the estimation of class probabilities (or target mean in the regression case) based on a hold-out set (backfitting) and does not prune the tree.

REP Tree:

 REP Tree is a fast decision tree learner, usually considered as an extension of C45 tree.  Sorting values for numeric  attributes once, it  builds a decision/regression tree using information gain/variance and  performs  reduced-error pruning, with backfitting. 

Random Forest:

Random Forest uses the bagging approach  to reduce variance and performs classification and regression.

 

The data requirements conditions for each model are documented through messages, tool tips, and documentation. FAQs and other relevant documents can be found on the AroniSoft (http://www.aronisoft.com ) Website.

Below are a few  an example of Decision Trees and Related Models, executed in AroniSmartIntelligence™.


Speech Translation Example

Figure 1: Data 


Speech Translation Example

Figure 2: Random Forest Results: Random Tree 


Speech Translation Example

Figure 3: Random Forest Results:   Statistics  


Speech Translation Example

Figure 4: Multinomial Logistic Model:  Performance Statistics  - Odds Ratio


Speech Translation Example

Figure 5: Bayesian Neural Network Model:  Statistics and Network


Speech Translation Example

Figure 6: REP Tree Model:  Tree


FOR QUICK DEMOS ON HANDBOOK and USER'S MANUALS,  PLEASE CLICK ON THE LINKS BELOW TO DOWNLOAD THE PDF FILES:


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What is next?

The new version of AroniSmartIntelligence™ that includes improved Advanced Analytics, Machine Learning and Data Science  is available on Apple's App Store®.

More detailed information on the capabilities of  AroniSmartIntelligence™ is available in the tool on Apple®'s App Store and on the AroniSoft web site(http://www.aronisoft.com).  

Check the Frequently Asked Questions (FAQ) here.

More detailed use cases, real life applications,  and AroniSmart Tools in actions  can be found on  on the AroniSmart web site (http://www.aronismart.com). Check the following tabs: ABOUT US, SOLUTIONS, SCITECH, MONEY


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