O'Reilly on Data

JUNE 18, 2019

The good news is that researchers from academia recently managed to leverage that large body of work and combine it with the power of scalable statistical inference for data cleaning. HoloClean adopts the well-known “noisy channel” model to explain how data was generated and how it was “polluted.”

Machine Learning 259

Machine Learning Data Quality Statistics Modeling 259

O'Reilly on Data

MAY 18, 2020

And last is the probabilistic nature of statistics and machine learning (ML). Most AI models decay overtime: This phenomenon, known more widely as model decay , refers to the declining quality of AI system results over time, as patterns in new data drift away from patterns learned in training data.

Risk 359

Risk Modeling Data Processing Software 359

Jedox

MAY 17, 2022

Financial planners , Chief Financial Officers, and analysts have all struggled to build accurate methods for predicting what’s likely to happen. Prior to the dawn of advanced statistical analysis and machine learning, predictive analytics efforts fell into 4 broad categories: Guessing , which is the default that most people revert to.

Predictive Analytics 81

Predictive Analytics Finance Forecasting Analytics 81

O'Reilly on Data

MARCH 23, 2021

One is how it gave rise to new forms of information flow: the vision of a novel space in which anybody could publish anything and everyone could find it. On top of this, pre-existing societal biases are being reinforced and promulgated at previously unheard of scales as we increasingly integrate machine learning models into our daily lives.

Internet Publishing and Broadcasting 317

Internet Publishing and Broadcasting Broadcasting Data-driven Interactive 317

Insight

AUGUST 5, 2020

Photo by Devon Divine on Unsplash Originally published in Maslo - Your Virtual Self. Summary statistics (i.e. This created a summary features matrix of 7472 recordings x 176 summary features, which was used for training emotion label prediction models. up to 20% for prediction of ‘happy’ in females?—?in

Predictive Modeling 98

Predictive Modeling Statistics Modeling Measurement 98

Smarten

NOVEMBER 19, 2018

Predictive analytics can help a business understand the buying behavior of its customers and prospects and plug n’ play predictive and forecasting tools help businesses to create Citizen Data Scientists and establish metrics and goals across the enterprise for uniform execution and understanding of business objectives.

Business Objectives 40

Business Objectives Predictive Analytics Analytics Forecasting 40

Smarten

APRIL 19, 2024

The use of Generative AI, LLM and products such as ChatGPT capabilities has been applied to all kinds of industries, from publishing and research to targeted marketing and healthcare. Nothing…and I DO mean NOTHING…is more prominent in technology buzz today than Artificial Intelligence (AI). billion, with the market growing by 31.1%

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

Domino Data Lab

NOVEMBER 17, 2020

Classical statistics, developed in the 20 th century for small datasets, do not work for data where the number of variables is much larger than the number of samples (Large P Small N, Curse of Dimensionality, or P >> N data). Predictive models fit to noise approach 100% accuracy. Antimicrobial. Autoimmunity. IL-4, IL-13.

Statistics 131

Statistics Measurement Testing Predictive Modeling 131

O'Reilly on Data

MARCH 20, 2019

The objective here is to brainstorm on potential security vulnerabilities and defenses in the context of popular, traditional predictive modeling systems, such as linear and tree-based models trained on static data sets. If an attacker can receive many predictions from your model API or other endpoint (website, app, etc.),

Modeling 222

Modeling Machine Learning Predictive Modeling Consulting 222

Data Science and Beyond

JULY 22, 2018

Two years later, I published a post on my then-favourite definition of data science , as the intersection between software engineering and statistics. I was very comfortable with that definition, having spent my PhD years on several predictive modelling tasks, and having worked as a software engineer prior to that.

Data Science 79

Data Science Machine Learning Statistics Predictive Modeling 79

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

Nor can we learn prediction intervals across a large set of parallel time series, since we are trying to generate intervals for a single global time series. With those stakes and the long forecast horizon, we do not rely on a single statistical model based on historical trends.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

Insight

FEBRUARY 19, 2020

The values 500 and N /2 are somewhat arbitrary but were chosen in order to obtain a smooth distribution of ROI values and to balance the desire for sufficient variability in the predictions with the need to maintain a large enough training set for each model. A schematic diagram of my modeling process is shown below.

Risk 67

Risk ROI Modeling Metrics 67

Domino Data Lab

JULY 9, 2019

Diving into examples of building and deploying ML models at The New York Times including the descriptive topic modeling-oriented Readerscope (audience insights engine), a prediction model regarding who was likely to subscribe/cancel their subscription, as well as prescriptive example via recommendations of highly curated editorial content.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

Jet Global

MAY 1, 2023

Advanced Analytics Some apps provide a unique value proposition through the development of advanced (and often proprietary) statistical models. Advanced Analytics Provide the unique benefit of advanced (and often proprietary) statistical models in your app. They can then pinpoint areas for improvement.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52