CIO Business Intelligence

OCTOBER 13, 2023

Responsibilities include building predictive modeling solutions that address both client and business needs, implementing analytical models alongside other relevant teams, and helping the organization make the transition from traditional software to AI infused software.

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

DataRobot Blog

APRIL 29, 2022

With the big data revolution of recent years, predictive models are being rapidly integrated into more and more business processes. When business decisions are made based on bad models, the consequences can be severe. Figure 1: The main components of a model as defined by banking industry regulators.

Risk 111

Risk Modeling IT Risk Management 111

O'Reilly on Data

DECEMBER 12, 2019

Model debugging is an emergent discipline focused on finding and fixing problems in ML systems. In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. Interpretable ML models and explainable ML.

Machine Learning 187

Machine Learning Modeling Testing Risk Management 187

DataRobot Blog

DECEMBER 1, 2022

Training and Testing Different AI Models. As DataRobot starts building predictive models, a large repository of open source and proprietary packages will experiment with various modeling techniques. The model selection process will test several models to see which one is likely to yield the best results.

Machine Learning 52

Machine Learning Marketing Modeling Statistics 52

datapine

NOVEMBER 27, 2019

The demand for real-time online data analysis tools is increasing and the arrival of the IoT (Internet of Things) is also bringing an uncountable amount of data, which will promote the statistical analysis and management at the top of the priorities list. But collaborative BI does not only remain around some documents’ exchanges or updates.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

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

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

OCTOBER 23, 2019

As a result, there has been a recent explosion in individual statistics that try to measure a player’s impact. You can add a Makefile command test that will run all of your notebooks by issuing. test: py.test --nbval notebooks/*.ipynb. 05) in predicting changes in attendance. The lower the RMSE, the better the prediction.

Statistics 59

Statistics Machine Learning Testing Modeling 59

Domino Data Lab

AUGUST 20, 2019

The problem is that a new unique identifier of a test example won’t be anywhere in the tree. Feature extraction means moving from low-level features that are unsuitable for learning—practically speaking, we get poor testing results—to higher-level features which are useful for learning. Separate out a hold-out test set.

Testing 68

Testing Modeling Interactive Measurement 68

Domino Data Lab

AUGUST 22, 2019

There are steps you can take to preprocess natural language data such that the modeling you carry out downstream may be more accurate. Common natural language preprocessing options include: Tokenization: This is the splitting of a document (e.g., Execute gutenberg.fileids() to print the names of all 18 documents.) Example 11.6

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

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. Read carefully.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52