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Dimension modelling in detail is described in this presentation. Bridge tables Fact tables star schema snowflake schema is explianed. Kimball is the reference book for the source contents
The slides describes a new technique used to store daily snapshot of data, without really taking daily snapshot but using Temporal Data theory in order to store intervals of data instead point-in-time information. This allows a dramatic reduction of stored rows, enabling the creation of big data solution reducing the hardware costs
Dimension modelling in detail is described in this presentation. Bridge tables Fact tables star schema snowflake schema is explianed. Kimball is the reference book for the source contents
The slides describes a new technique used to store daily snapshot of data, without really taking daily snapshot but using Temporal Data theory in order to store intervals of data instead point-in-time information. This allows a dramatic reduction of stored rows, enabling the creation of big data solution reducing the hardware costs
Get to know in detail the termonologies of Random Forest with their types of algorithms used in the workflow along with their advantages and disadvantages of their predecessors.
Thanks, for your time, if you enjoyed this short article there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Classification algorithms play an important role in different business areas, such as fraud detection, cross selling or customer behavior. In the business context, interpretability is a very desirable property, sometimes even a hard requirement. However, interpretable algorithms are usually outperformed by other non-interpretable algorithms such as Random Forest. In this talk Antonio Soriano and Mateo Alvarez presented a distributed implementation in Spark of the Logistic Model Tree (LMT) algorithm (Landwehr, et al. (2005). Machine Learning, 59(1-2), 161-205.), which consists of a decision tree with logistic classifiers in the leaves. While being highly interpretable, the LMT consistently performs equally or better than other popular algorithms in several performance metrics such as accuracy, precision/recall or area under the ROC curve.
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An Introduction to Random Forest and linear regression algorithmsShouvic Banik0139
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Data Mining Module 3 Business Analtics..pdfJayanti Pande
Business Analytics Paper 2
| Data Mining | RTMNU Nagpur University MBA | Module 3
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This lecture provides an overview of decision tree machine learning algorithms and random forest ensemble techniques. The practical example includes diagnosing Type II diabetes and evaluating customer churn in the telecommunication industry.
One of the most powerful ways to apply advanced analytics is by putting them to work in operational systems. Using analytics to improve the way every transaction, every customer, every website visitor is handled is tremendously effective. The multiplicative effect means that even small analytic improvements add up to real business benefit.
This is the slide deck from the Webinar. James Taylor, CEO of Decision Management Solutions, and Dean Abbott of Abbott Analytics discuss 10 best practices to make sure you can effectively build and deploy analytic models into you operational systems. webinar recording available here: https://decisionmanagement.omnovia.com/archives/70931
Extension of this method exists in recent paper here: https://arxiv.org/ftp/arxiv/papers/1708/1708.05712.pdf
Overview and tutorial of Morse-Smale regression prior to a new paper coming out exploring this idea further. It is a topologically-based piecewise regression method for supervised learning.
The difficulty faced in Champion's southeastern regions is that the majority of the forest land has been converted to large, uniform plantations that exceed the maximum opening size restrictions under S&S. In order to use the Remsoft blocking tools effectively, these large tracts need to be subdivided into smaller units. However, since the topography in the area is virtually flat with minimal variation in site quality, natural boundaries could not be used to subdivide plantations. Instead, a systematic method of subdivision had to be developed.
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Improve Your Regression with CART and RandomForestsSalford Systems
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Thanks, for your time, if you enjoyed this short article there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Classification algorithms play an important role in different business areas, such as fraud detection, cross selling or customer behavior. In the business context, interpretability is a very desirable property, sometimes even a hard requirement. However, interpretable algorithms are usually outperformed by other non-interpretable algorithms such as Random Forest. In this talk Antonio Soriano and Mateo Alvarez presented a distributed implementation in Spark of the Logistic Model Tree (LMT) algorithm (Landwehr, et al. (2005). Machine Learning, 59(1-2), 161-205.), which consists of a decision tree with logistic classifiers in the leaves. While being highly interpretable, the LMT consistently performs equally or better than other popular algorithms in several performance metrics such as accuracy, precision/recall or area under the ROC curve.
Hadoop & Greenplum: Why Do Such a Thing?Ed Kohlwey
Greenplum is using Hadoop in several interesting ways as part of a larger big data architecture with EMC Greenplum Database (a scale-out MPP SQL database) and EMC Isilon (a scale-out network-attached storage appliance). After a quick introduction of Greenplum Database and Isilon, I list some ways Greenplum is tightly integrating with Hadoop and why we would want to do such a thing. Integration points discussed include: Greenplum Database external tables to seamlessly access data in HDFS, querying HBase tables natively from Greenplum Database, Greenplum Database having its underlying storage on HDFS, and Isilon OneFS as a seamless replacement for HDFS.
An Introduction to Random Forest and linear regression algorithmsShouvic Banik0139
This presentation aims to provide a comprehensive understanding of the Random Forest and Linear Regression algorithms, their functioning, and significance. It is designed to equip the audience with the knowledge required to apply these algorithms effectively in practical scenarios, and to further enhance their expertise in the field.
Data Mining Module 3 Business Analtics..pdfJayanti Pande
Business Analytics Paper 2
| Data Mining | RTMNU Nagpur University MBA | Module 3
| Decision Trees and Decision Rules | By Jayanti Pande | ProNotesJRP | JRP Notes
Data Science - Part V - Decision Trees & Random Forests Derek Kane
This lecture provides an overview of decision tree machine learning algorithms and random forest ensemble techniques. The practical example includes diagnosing Type II diabetes and evaluating customer churn in the telecommunication industry.
One of the most powerful ways to apply advanced analytics is by putting them to work in operational systems. Using analytics to improve the way every transaction, every customer, every website visitor is handled is tremendously effective. The multiplicative effect means that even small analytic improvements add up to real business benefit.
This is the slide deck from the Webinar. James Taylor, CEO of Decision Management Solutions, and Dean Abbott of Abbott Analytics discuss 10 best practices to make sure you can effectively build and deploy analytic models into you operational systems. webinar recording available here: https://decisionmanagement.omnovia.com/archives/70931
Extension of this method exists in recent paper here: https://arxiv.org/ftp/arxiv/papers/1708/1708.05712.pdf
Overview and tutorial of Morse-Smale regression prior to a new paper coming out exploring this idea further. It is a topologically-based piecewise regression method for supervised learning.
The difficulty faced in Champion's southeastern regions is that the majority of the forest land has been converted to large, uniform plantations that exceed the maximum opening size restrictions under S&S. In order to use the Remsoft blocking tools effectively, these large tracts need to be subdivided into smaller units. However, since the topography in the area is virtually flat with minimal variation in site quality, natural boundaries could not be used to subdivide plantations. Instead, a systematic method of subdivision had to be developed.
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https://alandix.com/academic/papers/synergy2024-epistemic/
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Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
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But there’s more:
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Connector Corner: Automate dynamic content and events by pushing a button
TreeNet Tree Ensembles & CART Decision Trees: A Winning Combination
1. TreeNet Tree
Ensembles and CART
Decision Trees: A
Winning Combination
October 2012
Mikhail Golovnya
Salford Systems
CART® software is a trademark of California Statistical Software, Inc. and is licensed exclusively to Salford Systems.
TreeNet® software is a trademark of Salford Systems