This document introduces data partitioning in XLMiner. Partitioning divides a large dataset into training, validation, and test sets. The training set builds a model, the validation set checks the model's accuracy, and the test set determines real-world performance. XLMiner allows standard and oversampled partitioning with options for automatic, specified, or equal ratios across the partitions.
DATA PARTITIONING FOR ENSEMBLE MODEL BUILDINGijccsa
In distributed ensemble model-building algorithms, the performance and statistical validity of models are
dependent on sizes of the input data partitions as well as the distribution of records among the partitions.
Failure to correctly select and pre-process the data often results in the models which are not stable and do
not perform well. This article introduces an optimized approach to building the ensemble models for very
large data sets in distributed map-reduce environments using Pass-Stream-Merge (PSM) algorithm. To
ensure the model correctness the input data is randomly distributed using the facilities built into mapreduce
frameworks.
Accuracy assessment is an important part of any classification project. It compares the classified image to another data source that is considered to be accurate or ground truth data. Ground truth can be collected in the field; however, this is time consuming and expensive. Ground truth data can also be derived from interpreting high-resolution imagery, existing classified imagery, or GIS data layers.
The most common way to assess the accuracy of a classified map is to create a set of random points from the ground truth data and compare that to the classified data in a confusion matrix. Although this is a two-step process, you may need to compare the results of different classification methods or training sites, or you may not have ground truth data and are relying on the same imagery that you used to create the classification. To accommodate these other workflows, this process uses three geoprocessing tools: Create Accuracy Assessment Points, Update Accuracy Assessment Points, and Compute Confusion Matrix.
Thresholding
Thresholding is the process of identifying the pixels in a classified image that are the most likely to be classified incorrectly. These pixels are put into another class (usually class 0). These pixels are identified statistically, based upon the distance measures
that were used in the classification decision rule.
Accuracy Assessment : Error Matrix
Accuracy assessment is a general term for comparing the classification to geographical data that are assumed
to be true, in order to determine the accuracy of the classification process. Usually, the assumed-true data are derived from ground truth data. It is usually not practical to ground truth or otherwise test every pixel of a classified image. Therefore, a set of reference pixels is usually used. Reference pixels are points on the classified image for which actual data are (or will be) known. The reference pixels are randomly selected.
Overall accuracy: Overall accuracy is used to indicate the accuracy of whole classification (i.e. number of correctly classifier pixels divided by the total number of pixels in the error matrix)
User’s accuracy(commission error): User’s accuracy is regarded as the probability that a pixel classified on map actually represents that
class on the ground or reference data
Producer’s accuracy(omission error): Producer’s accuracy represents the probability of reference pixel being correctly classified
THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
PANORAMA NECTO 14 TRAINING - Panorama is leading a Business Intelligence 3.0 revolution and a creation of a new generation of Business Intelligence & Data Discovery solutions that enable organizations to leverage the power of Social Decision Making and Automated Intelligence to gain insights more quickly, more efficiently, and with greater relevancy.
www.panorama.com
DATA PARTITIONING FOR ENSEMBLE MODEL BUILDINGijccsa
In distributed ensemble model-building algorithms, the performance and statistical validity of models are
dependent on sizes of the input data partitions as well as the distribution of records among the partitions.
Failure to correctly select and pre-process the data often results in the models which are not stable and do
not perform well. This article introduces an optimized approach to building the ensemble models for very
large data sets in distributed map-reduce environments using Pass-Stream-Merge (PSM) algorithm. To
ensure the model correctness the input data is randomly distributed using the facilities built into mapreduce
frameworks.
Accuracy assessment is an important part of any classification project. It compares the classified image to another data source that is considered to be accurate or ground truth data. Ground truth can be collected in the field; however, this is time consuming and expensive. Ground truth data can also be derived from interpreting high-resolution imagery, existing classified imagery, or GIS data layers.
The most common way to assess the accuracy of a classified map is to create a set of random points from the ground truth data and compare that to the classified data in a confusion matrix. Although this is a two-step process, you may need to compare the results of different classification methods or training sites, or you may not have ground truth data and are relying on the same imagery that you used to create the classification. To accommodate these other workflows, this process uses three geoprocessing tools: Create Accuracy Assessment Points, Update Accuracy Assessment Points, and Compute Confusion Matrix.
Thresholding
Thresholding is the process of identifying the pixels in a classified image that are the most likely to be classified incorrectly. These pixels are put into another class (usually class 0). These pixels are identified statistically, based upon the distance measures
that were used in the classification decision rule.
Accuracy Assessment : Error Matrix
Accuracy assessment is a general term for comparing the classification to geographical data that are assumed
to be true, in order to determine the accuracy of the classification process. Usually, the assumed-true data are derived from ground truth data. It is usually not practical to ground truth or otherwise test every pixel of a classified image. Therefore, a set of reference pixels is usually used. Reference pixels are points on the classified image for which actual data are (or will be) known. The reference pixels are randomly selected.
Overall accuracy: Overall accuracy is used to indicate the accuracy of whole classification (i.e. number of correctly classifier pixels divided by the total number of pixels in the error matrix)
User’s accuracy(commission error): User’s accuracy is regarded as the probability that a pixel classified on map actually represents that
class on the ground or reference data
Producer’s accuracy(omission error): Producer’s accuracy represents the probability of reference pixel being correctly classified
THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
PANORAMA NECTO 14 TRAINING - Panorama is leading a Business Intelligence 3.0 revolution and a creation of a new generation of Business Intelligence & Data Discovery solutions that enable organizations to leverage the power of Social Decision Making and Automated Intelligence to gain insights more quickly, more efficiently, and with greater relevancy.
www.panorama.com
EXTRACTION OF SEQUENTIAL RULES (VIDEO 4/4)Alexis Bondu
This video presents the MODL approch devised for the preparation of the sequential data (texts, web sessions, logs…). The aim is to identify the sub-sequences of the data set allowing to describe in a precise and robust way the distribution of the classes to be predicted (Auto Features Engineering).
How to transform and select variables/features when creating a predictive model using machine learning. To see the source code visit https://github.com/Davisy/Feature-Engineering-and-Feature-Selection
EXTRACTION OF SEQUENTIAL RULES (VIDEO 4/4)Alexis Bondu
This video presents the MODL approch devised for the preparation of the sequential data (texts, web sessions, logs…). The aim is to identify the sub-sequences of the data set allowing to describe in a precise and robust way the distribution of the classes to be predicted (Auto Features Engineering).
How to transform and select variables/features when creating a predictive model using machine learning. To see the source code visit https://github.com/Davisy/Feature-Engineering-and-Feature-Selection
Machine learning session6(decision trees random forrest)Abhimanyu Dwivedi
Concepts include decision tree with its examples. Measures used for splitting in decision tree like gini index, entropy, information gain, pros and cons, validation. Basics of random forests with its example and uses.
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.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
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.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
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
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
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.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Knowledge engineering: from people to machines and back
XL-MINER:Partition
1. Introduction to XLMiner™: PARTITION DATA XLMiner and Microsoft Office are registered trademarks of the respective owners.
2. Introduction to Partition Data Generally the data sets used in mining are enormous. Hence in order to mine data easily ,one method is to divide/partition data. Partitioning data means dividing the data set into multiple partitions that are mutually exclusive i.e. they do not overlap or the partitions have no data records are common. Partitioning data generally results in 3 sets of data: Training Data set :- This partition is used to create/build the mining model. Validation Data set :- : It is used to check whether the model developed using the training set is accurate or not. The validation set consists of data whose result (the value of the variable to be determined) is already known so that results obtained after applying the model and the actual results can be matched. Test data set :- It is used to determine how the model would perform when it encounters real world data. http://dataminingtools.net
3.
4. Specify percentages :Unlike automatic, if selected ,the user can specify the ratio of the partitions created in terms of percentages.
5. Equal partitions: Selecting this option sets a partitioning ratio of 33.3(training): 33.3(validation): 33.3(test) .Partition with oversampling: This method of partitioning is used when the percentage of successes in the output variable is very low in the dataset but we want to train the data with a particular percentage of successes. http://dataminingtools.net
6. Data Set used for Partition http://dataminingtools.net
9. Standard Partition (Specify)-Step 1 Selecting “Specify percentages” allows us to set the partitioning ratios as per our need. Here we have set a ratio of 50(testing):30(validation):20(test) http://dataminingtools.net
10. Standard Partition (Equal)-Step 1 Selecting “Equal” sets the partitioning ratio at 33.3% for each partition creating 3 equal sized partitions. http://dataminingtools.net
11. Oversampled Partition – Data Set In order to oversample a data set, it must contain at least 1 data item that accepts only 2 distinct values, not more and only then can it be used as the success class(the data item which is oversampled) http://dataminingtools.net
13. Oversampled Partition – Output The records in the training data set http://dataminingtools.net
14. Oversampled Partition – Output Rows in Validation set = 27, Rows in testing set = 30% of 27 = 12. http://dataminingtools.net
15. Thank you For more visit: http://dataminingtools.net http://dataminingtools.net
16. 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