CIO Business Intelligence

JUNE 7, 2022

Predictive analytics applies techniques such as statistical modeling, forecasting, and machine learning to the output of descriptive and diagnostic analytics to make predictions about future outcomes. It is frequently used for risk analysis. This has the added benefit of often uncovering hidden patterns.

Data Analytics 123

Data Analytics Diagnostic Analytics Management Analytics 123

IBM Big Data Hub

DECEMBER 20, 2023

For instance, if data scientists were building a model for tornado forecasting, the input variables might include date, location, temperature, wind flow patterns and more, and the output would be the actual tornado activity recorded for those days. temperature, salary).

Machine Learning 75

Machine Learning Modeling Deep Learning Predictive Modeling 75

O'Reilly on Data

MARCH 24, 2020

John Myles White , data scientist and engineering manager at Facebook, wrote: “The biggest risk I see with data science projects is that analyzing data per se is generally a bad thing. The assumed value of data is a myth leading to inflated valuations of start-ups capturing said data.

Machine Learning 343

Machine Learning Statistics Data Quality Modeling 343

Domino Data Lab

AUGUST 1, 2021

The interest in interpretation of machine learning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. Methods for explaining Deep Learning.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

IBM Big Data Hub

AUGUST 28, 2023

Text representation In this stage, you’ll assign the data numerical values so it can be processed by machine learning (ML) algorithms, which will create a predictive model from the training inputs. And with advanced software like IBM Watson Assistant , social media data is more powerful than ever.

Data mining 54

Data mining Machine Learning Deep Learning Marketing 54

Domino Data Lab

JULY 28, 2021

There are many software packages that allow anyone to build a predictive model, but without expertise in math and statistics, a practitioner runs the risk of creating a faulty, unethical, and even possibly illegal data science application. All models are not made equal.

Data Science 110

Data Science Statistics Recreation/Entertainment Predictive Analytics 110

IBM Big Data Hub

FEBRUARY 14, 2024

Automotive With applications of AI, automotive manufacturers are able to more effectively predict and adjust production to respond to changes in supply and demand. They can streamline workflows to increase efficiency and reduce time-consuming tasks and the risk of error in production, support, procurement and other areas.

Cost-Benefit 83

Cost-Benefit Insurance Unstructured Data Machine Learning 83

IBM Big Data Hub

OCTOBER 16, 2023

Machine learning in financial transactions ML and deep learning are widely used in banking, for example, in fraud detection. Banks and other financial institutions train ML models to recognize suspicious online transactions and other atypical transactions that require further investigation.

Machine Learning 49

Machine Learning Marketing Forecasting Modeling 49

Smart Data Collective

MAY 17, 2021

All that performance data can be fed into a machine learning tool specifically designed to identify certain events, failures or obstacles. Predictive models, estimates and identified trends can all be sent to the project management team to speed up their decisions. That’s also where big data can step in and vastly expand ops.

Big Data 100

Big Data Cost-Benefit IoT Machine Learning 100

Alation

APRIL 14, 2022

They strove to ramp up skills in all manner of predictive modeling, machine learning, AI, or even deep learning. These factors risk data originating in far-flung environments, where the data structures and semantics are not well understood or documented. It’s not a simple definition.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

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

Domino Data Lab

APRIL 21, 2021

Rules-based fraud detection (top) vs. classification decision tree-based detection (bottom): The risk scoring in the former model is calculated using policy-based, manually crafted rules and their corresponding weights. deep learning) there is no guaranteed explainability.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

O'Reilly on Data

SEPTEMBER 11, 2019

For example, even though ML and ML-related concepts —a related term, “ML models,” (No. Deep learning,” for example, fell year over year to No. But the database—or, more precisely, the data model —is no longer the sole or, arguably, the primary focus of data engineering. 40; it peaked at Strata NY 2018 at No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20