The document discusses Microsoft's decision trees algorithm and its use for classification, regression, and association mining. It provides examples of DMX queries for classification models predicting school plans based on student attributes, regression models predicting parent income, and association models for customer dance preferences. It also covers interpreting decision tree model content, parameters for controlling tree growth and shape, and stored procedures for viewing and manipulating decision tree models.
Fuzzy Querying Based on Relational DatabaseIOSR Journals
The traditional query in relational database is unable to satisfy the needs for dealing with fuzzy
linguistic values. In this paper, a new data query technique composed of fuzzy theory and MS-SQL is provided.
Here, the query can be implemented for fuzzy linguistic variables query via an interface to Microsoft ASP.NET.
It is being applied to an realistic instance i.e. questions could be expressed by fuzzy linguistic values such as
young age, high blood pressure, average heart beat etc in Patients’ relational database. This could be widely
used to realize the other fuzzy query based on database.
This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques
Fuzzy Querying Based on Relational DatabaseIOSR Journals
The traditional query in relational database is unable to satisfy the needs for dealing with fuzzy
linguistic values. In this paper, a new data query technique composed of fuzzy theory and MS-SQL is provided.
Here, the query can be implemented for fuzzy linguistic variables query via an interface to Microsoft ASP.NET.
It is being applied to an realistic instance i.e. questions could be expressed by fuzzy linguistic values such as
young age, high blood pressure, average heart beat etc in Patients’ relational database. This could be widely
used to realize the other fuzzy query based on database.
This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques
Cognitive Database: An Apache Spark-Based AI-Enabled Relational Database Syst...Databricks
We describe design and implementation of Cognitive Database, a Spark-based relational database that demonstrates novel capabilities of AI-enabled SQL queries. A key aspect of our approach is to first view the structured data source as meaningful unstructured text, and then use the text to build an unsupervised neural network model using a Natural Language Processing (NLP) technique called word embedding. We seamlessly integrate the word embedding model into existing SQL query infrastructure and use it to enable a new class of SQL-based analytics queries called cognitive intelligence (CI) queries.
CI queries use the model vectors to enable complex queries such as semantic matching, inductive reasoning queries such as analogies/semantic clustering, predictive queries using entities not present in a database, and, more generally, using knowledge from external sources. We demonstrate unique capabilities of Cognitive Databases using an Apache Spark 2.2.0 based prototype to execute inductive reasoning CI queries over a multi-modal relational database containing text and images from the ImageNet dataset. We illustrate key aspects of the Spark-based implementation, e.g., UDF implementations of various cognitive functions using Spark SQL, Python (via Jupyter notebook) and Scala based interfaces, Distributed Spark implementation, and integration of GPU-enabled nearest neighbor kernels.
We also discuss a variety of real-world use cases from different application domains. Further details of this system can be found in the Arxiv paper: https://arxiv.org/abs/1712.07199
This presentation explores the potential of machine learning in predicting the severity of road accidents. We will delve into the data analysis process, the chosen machine learning algorithms, and the evaluation of our model's performance. This project aims to contribute to improved emergency response times and accident prevention strategies. visit for more: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
This presentation educates you about R - Decision Tree, Examples of use of decision tress with basic syntax, Input Data and out data with chart.
For more topics stay tuned with Learnbay.
Random forest algorithm for regression a beginner's guideprateek kumar
Two popular families of ensemble methods
BAGGING
Several estimators are built independently on subsets of the data and their predictions are averaged. Typically, the combined estimator is usually better than any of the single base estimator.
Bagging can reduce variance with little to no effect on bias.
ex: Random Forests
BOOSTING
Base estimators are built sequentially. Each subsequent estimator focuses on the weaknesses of the previous estimators. In essence, several weak models “team up” to produce a powerful ensemble model.
Boosting can reduce bias without incurring higher variance.
ex: Gradient Boosted Trees, AdaBoost
Bagging
The ensemble method we will be using today is called bagging, which is short for bootstrap aggregating.
Bagging builds multiple base models with resampled training data with replacement. We train k base classifiers on k different samples of training data. Using random subsets of the data to train base models promotes more differences between the base models.
We can use the BaggingRegressor class to form an ensemble of regressors. One such Bagging algorithms are random forest regressor. A random forest regressor is a meta estimator that fits a number of classifying decision trees on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is always the same as the original input sample size but the samples are drawn with replacement if bootstrap=True (default).
Random Forest Regressors uses some kind of splitting criterion to measure the quality of a split. Supported criteria are “MSE” for the mean squared error, which is equal to variance reduction as feature selection criterion, and “Mean Absolute Error” for the mean absolute error.
Problem Statement:
To predict the median prices of homes located in the Boston area given other attributes of the house
Analyzing and Visualizing Data Chapter 6Data Represent.docxdurantheseldine
Analyzing and Visualizing Data
Chapter 6
Data Representation
Introducing Visual Encoding
Data representation is the act of giving visual form to your data.
Viewers: When perceiving a visual display of data, it is decoded using the shapes, sizes, positions and colors to form an understanding
Visualizers: Doing the reverse through visual encoding, assigning visual properties to data values
Comprised of a combination of two properties
Marks: Visible features like dots, lines and areas
Attributes: Variations applied to the appearance of marks, such as size, position, or color.
Introducing Visual Encoding cont.
TBA
Introducing Visual Encoding cont.
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Introducing Visual Encoding cont.
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Introducing Visual Encoding cont.
TBA
Introducing Visual Encoding cont.
Marks and Attributes are the ingredients, a chart type is the recipe offering a predefined template for displaying data.
Different chart types offer different ways of representing data.
Introducing Visual Encoding cont.
TBA
Introducing Visual Encoding cont.
TBA
Introducing Visual Encoding cont.
TBA
Introducing Visual Encoding cont.
Chart Types
TBA
Chart Types
Exclusions
Inclusions
Categorical comparisons
Dual families
Text visualization
Dashboard
Small multiples
A note about ‘storytelling’
Influencing Factors and Considerations
TBA
Influencing Factors and Considerations cont.
TBA
Influencing Factors and Considerations cont.
TBA
Influencing Factors and Considerations cont.
TBA
Influencing Factors and Considerations cont.
TBA
Influencing Factors and Considerations cont.
TBA
Influencing Factors and Considerations cont.
TBA
Influencing Factors and Considerations cont.
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Influencing Factors and Considerations cont.
TBA
Influencing Factors and Considerations cont.
TBA
Influencing Factors and Considerations cont.
TBA
Influencing Factors and Considerations cont.
TBA
Analyzing and Visualizing Data
Selecting a Graph
Selecting a Graph
Pie Charts
Compare a certain sector to the total.
Useful when there are only two sectors, for example yes/no or queued/finished.
Instant understanding of proportions when few sectors are used as dimensions.
When you use 10 sectors, or less, the pie chart keeps its visual efficiency.
Selecting a Graph cont.
Bar Charts/Plots
Ordinal and nominal data sets
Compare things between different groups or to track changes over time
Measure change over time, bar graphs are best when the changes are larger
Display and compare the number, frequency or other measure (e.g. mean) for different discrete categories of data
Flexible chart type and there are several variations of the standard bar chart including horizontal bar charts, grouped or component charts, and stacked bar charts.
Frequency for each category of a categorical variable
Relative frequency (%) for each category
Select.
Comparing Colleges on basis of various attributes and doing regression using Weka Software
Demonstration of Clustering using Weka on various attributes on data set of places.
Use Machine learning to solve classification problems through building binary and multi-class classifiers.
Does your company face business-critical decisions that rely on dynamic transactional data? If you answered “yes,” you need to attend this free event featuring Microsoft analytics tools. We’ll focus on Azure Machine Learning capabilities and explore the following topics: - Introduction of two class classification problems.
- Classification Algorithms (Two Class Classification)
- Available algorithms in Azure ML.
- Real business problems that is solved using two class classification.
Bank - Loan Purchase Modeling
This case is about a bank which has a growing customer base. Majority of these customers are liability customers (depositors) with varying size of deposits. The number of customers who are also borrowers (asset customers) is quite small, and the bank is interested in expanding this base rapidly to bring in more loan business and in the process, earn more through the interest on loans. In particular, the management wants to explore ways of converting its liability customers to personal loan customers (while retaining them as depositors). A campaign that the bank ran last year for liability customers showed a healthy conversion rate of over 9% success. This has encouraged the retail marketing department to devise campaigns with better target marketing to increase the success ratio with a minimal budget. The department wants to build a model that will help them identify the potential customers who have a higher probability of purchasing the loan. This will increase the success ratio while at the same time reduce the cost of the campaign. The dataset has data on 5000 customers. The data include customer demographic information (age, income, etc.), the customer's relationship with the bank (mortgage, securities account, etc.), and the customer response to the last personal loan campaign (Personal Loan). Among these 5000 customers, only 480 (= 9.6%) accepted the personal loan that was offered to them in the earlier campaign.
Our job is to build the best model which can classify the right customers who have a higher probability of purchasing the loan. We are expected to do the following:
EDA of the data available. Showcase the results using appropriate graphs
Apply appropriate clustering on the data and interpret the output .
Build appropriate models on both the test and train data (CART & Random Forest). Interpret all the model outputs and do the necessary modifications wherever eligible (such as pruning).
Check the performance of all the models that you have built (test and train). Use all the model performance measures you have learned so far. Share your remarks on which model performs the best.
Similar to MS SQL SERVER: Decision trees algorithm (20)
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
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We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
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Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
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GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
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https://arxiv.org/abs/2306.08302
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https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
DevOps and Testing slides at DASA ConnectKari Kakkonen
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However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
2. Overview Decision Trees Algorithm DMX Queries Data Mining usingDecision Trees Model Content for a Decision Trees Model Decision Tree Parameters Decision Tree Stored Procedures
3. Decision Trees Algorithm The Microsoft Decision Trees algorithm is a classification and regression algorithm provided by Microsoft SQL Server Analysis Services for use in predictive modeling of both discrete and continuous attributes. For discrete attributes, the algorithm makes predictions based on the relationships between input columns in a dataset. It uses the values, known as states, of those columns to predict the states of a column that you designate as predictable. For example, in a scenario to predict which customers are likely to purchase a motor bike, if nine out of ten younger customers buy a motor bike, but only two out of ten older customers do so, the algorithm infers that age is a good predictor of the bike purchase.
4. Decision Trees Algorithm For continuous attributes, the algorithm uses linear regression to determine where a decision tree splits. If more than one column is set to predictable, or if the input data contains a nested table that is set to predictable, the algorithm builds a separate decision tree for each predictable column.
5. DMX Queries Lets understand how to use DMX queries by creating a simple tree model based on the School Plans data set. The table School Plans contains data about 500,000 high school students, including Parent Support, Parent Income, Sex, IQ, and whether or not the student plans to attend School. using the Decision Trees algorithm, you can create a mining model, predicting the School Plans attribute based on the four other attributes.
6. DMX Queries(Classification) CREATE MINING STRUCTURE SchoolPlans (ID LONG KEY, Sex TEXT DISCRETE, ParentIncome LONG CONTINUOUS, IQ LONG CONTINUOUS, ParentSupport TEXT DISCRETE, SchoolPlans TEXT DISCRETE ) WITH HOLDOUT (10 PERCENT) ALTER MINING STRUCTURE SchoolPlans ADD MINING MODEL SchoolPlan ( ID, Sex, ParentIncome, IQ, ParentSupport, SchoolPlans PREDICT ) USING Microsoft Decision Trees Model Creation:
7. DMX Queries(Classification) INSERT INTO SchoolPlans (ID, Sex, IQ, ParentSupport, ParentIncome, SchoolPlans) OPENQUERY(SchoolPlans, ‘SELECT ID, Sex, IQ, ParentSupport, ParentIncome, SchoolPlans FROM SchoolPlans’) Training the SchoolPlan Model
8. DMX Queries(Classification) SELECT t.ID, SchoolPlans.SchoolPlans, PredictProbability(SchoolPlans) AS [Probability] FROM SchoolPlans PREDICTION JOIN OPENQUERY(SchoolPlans, ‘SELECT ID, Sex, IQ, ParentSupport, ParentIncome FROM NewStudents’) AS t ON SchoolPlans.ParentIncome= t.ParentIncome AND SchoolPlans.IQ = t.IQ AND SchoolPlans.Sex= t.Sex AND SchoolPlans.ParentSupport= t.ParentSupport Predicting the SchoolPlan for a new student. This query returns ID, SchoolPlans, and Probability.
9. DMX Queries(Classification) SELECT t.ID, PredictHistogram(SchoolPlans) AS [SchoolPlans] FROM SchoolPlans PREDICTION JOIN OPENQUERY(SchoolPlans, ‘SELECT ID, Sex, IQ, ParentSupport, ParentIncome FROM NewStudents’) AS t ON SchoolPlans.ParentIncome= t.ParentIncome AND SchoolPlans.IQ = t.IQ AND SchoolPlans.Sex= t.Sex AND SchoolPlans.ParentSupport= t.ParentSupportn Query returns the histogram of the SchoolPlans predictions in the form of a nested table. Result of this query is shown in the next slide.
11. DMX Queries (Regression) Regression means predicting continuous variables using linear regression formulas based on regressors that you specify. ALTER MINING STRUCTURE SchoolPlans ADD MINING MODEL ParentIncome ( ID, Gender, ParentIncome PREDICT, IQ REGRESSOR, ParentEncouragement, SchoolPlans ) USING Microsoft Decision Trees INSERT INTO ParentIncome Creating and training a regression model to Predict ParentIncome using IQ, Sex, ParentSupport, and SchoolPlans. IQ is used as a regressor.
12. DMX Queries (Regression) SELECT t.StudentID, ParentIncome.ParentIncome, PredictStdev(ParentIncome) AS Deviation FROM ParentIncome PREDICTION JOIN OPENQUERY(SchoolPlans, ‘SELECT ID, Sex, IQ, ParentSupport, SchoolPlans FROM NewStudents’) AS t ON ParentIncome.SchoolPlans = t. SchoolPlans AND ParentIncome.IQ = t.IQ AND ParentIncome.Sex = t.Sex AND ParentIncome.ParentSupport = t. ParentSupport Continuous prediction using a decision tree to predict the ParentIncome for new students and the estimated standard deviation for each prediction.
13.
14. Each Show is considered an attribute with binary states— existing or missing.
15. DMX Queries(Association) INSERT INTO DanceAssociation ( ID, Gender, MaritalStatus, Shows (SKIP, Show)) SHAPE { OPENQUERY (DanceSurvey, ‘SELECT ID, Gender, [Marital Status] FROM Customers ORDER BY ID’) } APPEND ( {OPENQUERY (DanceSurvey, ‘SELECT ID, Show FROM Shows ORDER BY ID’)} RELATE ID TO ID )AS Shows Training an associative trees model Because the model contains a nested table, the training statement involves the Shape statement.
16. DMX Queries(Association) Training an associative trees model Suppose that there is a married male customer who likes the Michael Jackson’s Show. This query returns the other five Shows this customer is most likely to find appealing. SELECT t.ID, Predict(DanceAssociation.Shows,5, $AdjustedProbability) AS Recommendation FROM DanceAssociation NATURAL PREDICTION JOIN (SELECT ‘101’ AS ID, ‘Male’ AS Gender, ‘Married’ AS MaritalStatus, (SELECT ‘Michael Jackson’ AS Show) AS Shows) AS t
17. Data Mining usingDecision Trees The most common data mining task for a decision tree is classification i.e. determining whether or not a set of data belongs to a specific type, or class. The principal idea of a decision tree is to split your data recursively into subsets. The process of evaluating all inputs is then repeated on each subset. When this recursive process is completed, a decision tree is formed.
18. Data Mining usingDecision Trees Decision trees offer several advantages over other data mining algorithms. Trees are quick to build and easy to interpret. Each node in the tree is clearly labeled in terms of the input attributes, and each path formed from the root to a leaf forms a rule about your target variable. Prediction based on decision trees is efficient.
19. Model Content for a Decision Trees Model The top level is the model node. The children of the model node are its tree root nodes. If a tree model contains a single tree, there is only one node in the second level. The nodes of the other levels are either intermediate nodes (or leaf nodes) of the tree. The probabilities of each predictable attribute state are stored in the distribution row sets.
21. Interpreting the Mining Model Content A decision trees model has a single parent node that represents the model and its metadata underneath which are independent trees that represent the predictable attributes that you select. For example, if you set up your decision tree model to predict whether customers will purchase something, and provide inputs for gender and income, the model would create a single tree for the purchasing attribute, with many branches that divide on conditions related to gender and income. However, if you then add a separate predictable attribute for participation in a customer rewards program, the algorithm will create two separate trees under the parent node. One tree contains the analysis for purchasing, and another tree contains the analysis for the customer rewards program.
22. Decision Tree Parameters The tree growth, tree shape, and the input output attribute settings are controlled using these parameters . You can fine-tune your model’s accuracy by adjusting these parameter settings.
31. Decision Tree Stored Procedures GetTreeScores is the procedure that the Decision Tree viewer uses to populate the drop-down tree selector. It takes a name of a decision tree model as a parameter and returns a table containing a row for every tree on the model and the following three columns: ATTRIBUTE_NAMEis the name of the tree. NODE_UNIQUE_NAME is the content node representing the root of the tree. MSOLAP_NODE_SCORE is a number representing the amount of information(number of nodes) in the tree.
32. Decision Tree Stored Procedures DTGetNodes is used by the decision tree Dependency Network viewer when you click the Add Nodes button. It returns a row for all potential nodes in the dependency network and has the following two columns: NODE UNIQUE NAME1 is an identifier that is unique for the dependency network. NODE CAPTION is the name of the node.
33. Decision Tree Stored Procedures The DTGetNodeGraph procedure returns four columns: When a row has NODE TYPE = 1, it contains a description of the nodes and the remaining three columns have the following interpretation: NODE UNIQUE NAME1 contains a unique identifier for the node. NODE UNIQUE NAME2 contains the node caption. When a row has NODE TYPE = 2, it represents a directed edge in the graph and the remaining columns have these interpretations: NODE UNIQUE NAME1 contains the node name of the starting point of the edge. NODE UNIQUE NAME2 contains the node name of the ending point of the edge. MSOLAP NODE SCORE contains the relative weight of the edge.
34. Decision Tree Stored Procedures DTAddNodesallows you to add new nodes to an existing graph. It takes a model name, a semicolon-separated list of the IDs of nodes you want to add to the graph, and a semicolon-separated list of the IDs of nodes already in the graph. This procedure returns a table similar to the NODE TYPE = 2 section of DTGetNodeGraph, but without the NODE TYPE column. The rows in the result set contain all the edges between the added nodes, and all of the edges between the added nodes and the nodes specified as already in the graph.
35. Summary Decision Trees Algorithm Overview DMX Queries Data Mining usingDecision Trees Interpreting the Model Content for a Decision Trees Model Decision Tree Parameters Decision Tree Stored Procedures
36. Visit more self help tutorials Pick a tutorial of your choice and browse through it at your own pace. The tutorials section is free, self-guiding and will not involve any additional support. Visit us at www.dataminingtools.net