O'Reilly on Data

JUNE 18, 2019

There has been a significant increase in our ability to build complex AI models for predictions, classifications, and various analytics tasks, and there’s an abundance of (fairly easy-to-use) tools that allow data scientists and analysts to provision complex models within days.

Machine Learning 255

Machine Learning Data Quality Statistics Modeling 255

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 357

Risk Modeling Data Processing Software 357

Jedox

MAY 17, 2022

Predictive analytics is a discipline that’s been around in some form since the dawn of measurement. We’ve always been trying to predict the future; go back in history to look at prognosticators like Nostradamus and many other prophets. What is Predictive Analytics?

Predictive Analytics 83

Predictive Analytics Finance Analytics Forecasting 83

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. the Mel-frequency cepstrum).

Predictive Modeling 98

Predictive Modeling Statistics Modeling Measurement 98

Domino Data Lab

NOVEMBER 17, 2020

High throughput screening technologies have been developed to measure all the molecules of interest in a sample in a single experiment (e.g., Predictive models fit to noise approach 100% accuracy. Each of these behaviors wreak havoc on statistical analyses. Introduction. Pairwise distances between points become the same.

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 219

Modeling Machine Learning Predictive Modeling Consulting 219

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