Data mining, Knowledge Discovery and Modeling

Data mining

Knowledge Discovery

Modeling

Fundamentals of Data Mining

Data Science 101

OCTOBER 31, 2019

This data alone does not make any sense unless it’s identified to be related in some pattern. Data mining is the process of discovering these patterns among the data and is therefore also known as Knowledge Discovery from Data (KDD). Machine learning provides the technical basis for data mining.

Data mining

Data mining KDD Data Science Knowledge Discovery

KDD 2020 Opens Call for Papers

Data Science 101

DECEMBER 11, 2019

This weeks guest post comes from KDD (Knowledge Discovery and Data Mining). Every year they host an excellent and influential conference focusing on many areas of data science. Honestly, KDD has been promoting data science way before data science was even cool. 1989 to be exact. The details are below.

KDD

KDD Knowledge Discovery Data mining Statistics

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Webinars

Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications

The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Communication

From Developer Experience to Product Experience: How a Shared Focus Fuels Product Success

Beyond the Basics of A/B Tests: Innovative Experimentation Tactics You Need to Know as a Data or Product Professional

MORE WEBINARS

Trending Sources

Experiment design and modeling for long-term studies in ads

The Unofficial Google Data Science Blog

OCTOBER 7, 2015

by HENNING HOHNHOLD, DEIRDRE O'BRIEN, and DIANE TANG In this post we discuss the challenges in measuring and modeling the long-term effect of ads on user behavior. We describe experiment designs which have proven effective for us and discuss the subtleties of trying to generalize the results via modeling.

Modeling

Modeling Experimentation Knowledge Discovery Testing

Webinars

Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications

The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Communication

From Developer Experience to Product Experience: How a Shared Focus Fuels Product Success

Beyond the Basics of A/B Tests: Innovative Experimentation Tactics You Need to Know as a Data or Product Professional

MORE WEBINARS

How Do Super Rookies Start Learning Data Analysis?

FineReport

DECEMBER 19, 2019

For super rookies, the first task is to understand what data analysis is. Data analysis is a type of knowledge discovery that gains insights from data and drives business decisions. One is how to gain insights from the data. Data is cold and can’t speak. From Google. There are two points here.

Knowledge Discovery

Knowledge Discovery Visualization Reporting Experimentation

Variance and significance in large-scale online services

The Unofficial Google Data Science Blog

JANUARY 14, 2016

But the fact that a service could have millions of users and billions of interactions gives rise to both big data and methods which are effective with big data. Of particular interest to LSOS data scientists are modeling and prediction techniques which keep improving with more data.

Experimentation

Experimentation Statistics Metrics Measurement

Business Intelligence System: Definition, Application & Practice

FineReport

JULY 16, 2021

In addition, data warehouse provides a data storage environment where data onto multiple data sources will be ETLed(Extracted, Transformed, Dunked) , cleaned up, and stored on a specific topic, indicating powerful data integration and maintenance capabilities of BI. Data Analysis. Data Mining.

Business Intelligence

Business Intelligence Informatics Data Warehouse Data mining

Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

JULY 22, 2020

In practice, one may want to use more complex models to make these estimates. For example, one may want to use a model that can pool the epoch estimates with each other via hierarchical modeling (a.k.a. These MAB algorithms are great at maximizing reward when the models are perfectly specified and probabilities are accurate.

Experimentation

Experimentation Statistics Testing Strategy

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Domino Data Lab

MAY 20, 2021

In this article we discuss why fitting models on imbalanced datasets is problematic, and how class imbalance is typically addressed. Data mining for direct marketing: Problems and solutions. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, 73–79. Quinlan, J.

Machine Learning

Machine Learning Metrics Data mining Knowledge Discovery

Using Empirical Bayes to approximate posteriors for large "black box" estimators

The Unofficial Google Data Science Blog

NOVEMBER 4, 2015

But most common machine learning methods don’t give posteriors, and many don’t have explicit probability models. More precisely, our model is that $theta$ is drawn from a prior that depends on $t$, then $y$ comes from some known parametric family $f_theta$. Here, our items are query-ad pairs. Calculate posterior quantities of interest.

Testing

Testing KDD Machine Learning Modeling

Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

AUGUST 1, 2021

In this article we cover explainability for black-box models and show how to use different methods from the Skater framework to provide insights into the inner workings of a simple credit scoring neural network model. The interest in interpretation of machine learning has been rapidly accelerating in the last decade. See Ribeiro et al.

Modeling

Modeling Deep Learning Machine Learning Knowledge Discovery

LSOS experiments: how I learned to stop worrying and love the variability

The Unofficial Google Data Science Blog

FEBRUARY 29, 2016

Rare binary event example In the previous post , we discussed how rare binary events can be fundamental to the LSOS business model. Say we build a classifier to classify user sessions into two groups which we will call “dead” and “undead” to emphasize the importance of the rare purchase event to our business model.

Experimentation

Experimentation Statistics Metrics Measurement

Data Leaders Brief

Fundamentals of Data Mining

KDD 2020 Opens Call for Papers

Webinars

Trending Sources

Experiment design and modeling for long-term studies in ads

Webinars

How Do Super Rookies Start Learning Data Analysis?

Variance and significance in large-scale online services

Business Intelligence System: Definition, Application & Practice

Changing assignment weights with time-based confounders

ML internals: Synthetic Minority Oversampling (SMOTE) Technique

Using Empirical Bayes to approximate posteriors for large "black box" estimators

Explaining black-box models using attribute importance, PDPs, and LIME

LSOS experiments: how I learned to stop worrying and love the variability

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