O'Reilly on Data

JANUARY 17, 2023

Building Models. A common task for a data scientist is to build a predictive model. You’ll try this with a few other algorithms, and their respective tuning parameters–maybe even break out TensorFlow to build a custom neural net along the way–and the winning model will be the one that heads to production.

Machine Learning 325

Machine Learning Predictive Modeling Software Modeling 325

CIO Business Intelligence

MARCH 5, 2024

Summarizing meetings, emails, and documents Microsoft 365 will set the benchmark on copilot capabilities, given the speed at which it introduced embedded AI assistant features and the platform’s wide user base. Today, top AI-assistant capabilities delivering results include generating code, test cases, and documentation.

Cost-Benefit 124

Cost-Benefit Advertising Experimentation Software 124

Sisense

SEPTEMBER 25, 2020

Linear regression is a form of supervised learning (or predictive modeling). In supervised learning, the dependent variable is predicted from the combination of independent variables. When a single independent variable is used to predict the value of a dependent variable, it’s called simple linear regression. Clustering.

Statistics 98

Statistics Predictive Modeling Modeling Machine Learning 98

IBM Big Data Hub

DECEMBER 20, 2023

Unsupervised machine learning Unsupervised learning algorithms—like Apriori, Gaussian Mixture Models (GMMs) and principal component analysis (PCA)—draw inferences from unlabeled datasets, facilitating exploratory data analysis and enabling pattern recognition and predictive modeling.

Machine Learning 80

Machine Learning Modeling Deep Learning Predictive Modeling 80

DataRobot Blog

DECEMBER 15, 2022

While AI-powered forecasting can help retailers implement sales and demand forecasting—this process is very complex, and even highly data-driven companies face key challenges: Scale: Thousands of item combinations make it difficult to manually build predictive models. A variety of models are been trained in parallel.

Forecasting 81

Forecasting Modeling ROI Machine Learning 81

IBM Big Data Hub

JULY 5, 2023

Until recently, managers and technicians relied on manual tools like Excel spreadsheets and document tracking to accomplish this. This information is then used to build predictive models of asset performance over time and help spot potential problems before they arise.

Strategy 58

Strategy Management IoT Cost-Benefit 58

DataRobot Blog

DECEMBER 1, 2022

As you upload your data, DataRobot will do some initial exploratory data analysis to get a deeper understanding of the dataset prior to model training. Next, choose your target variable—in this instance it is automatically detected as a classification problem and an optimization metric is recommended. Automate Feature Engineering.

Machine Learning 52

Machine Learning Marketing Modeling Statistics 52

DataRobot Blog

JANUARY 10, 2023

Now that we have set up the notebook environment, let’s explore the capabilities available in DataRobot Notebooks while walking through the process of training and deploying a readmissions prediction model end-to-end.

Experimentation 52

Experimentation Machine Learning Data Science Modeling 52

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

Over the life of the forecast, the data scientist will publish historical accuracy metrics. But due to the long time lag between forecasts and actuals, these metrics alone are insufficient. Every forecast update will include metrics to provide insight on change drivers, and will flag significant gaps between different model forecasts.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

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.) 0.85 = 0.15.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

Domino Data Lab

OCTOBER 23, 2019

One of the things this data set doesn’t have, however, is a single metric to rank both offensive and defensive performance in a single statistic. A further diagnostic step is to plot the predicted values of the linear regression versus the actual values. In Figure 6.8, Collecting Wikipedia Pageviews for Athletes.

Statistics 59

Statistics Machine Learning Testing Modeling 59

Occam's Razor

NOVEMBER 18, 2014

Short story #2: Predictive Modeling, Quantifying Cost of Inaction. You can hover over each box to get a sense of the key metrics. Short story #2: Predictive Modeling, Quantifying Cost of Inaction. Thank goodness for predictive models. Short story #1: Treemaps, Sunbursts, Packed Trees, Oh My!

Visualization 143

Visualization Slice and Dice Predictive Modeling Metrics 143

Rocket-Powered Data Science

SEPTEMBER 20, 2021

We would be able to go far beyond searching for correctly spelled column headings in databases or specific keywords in data documentation, to find the data we needed (assuming we even knew the correct labels, metatags, and keywords used by the dataset creators).

Data Science 285

Data Science Forecasting Business Intelligence Sales 285

Domino Data Lab

AUGUST 1, 2021

Intrinsic methods – this technique is based on ANNs that have been designed to output an explanation alongside the standard prediction. Because of its architecture, intrinsically explainable ANNs can be optimised not just on its prediction performance, but also on its explainability metric. A14 : no checking account.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

datapine

NOVEMBER 27, 2019

Hotels try to predict the number of guests they can expect on any given night in order to adjust prices to maximize occupancy and increase revenue. The predictive models, in practice, use mathematical models to predict future happenings, in other words, forecast engines.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

Jet Global

MAY 1, 2023

As a result, end users can better view shared metrics (backed by accurate data), which ultimately drives performance. When treating a patient, a doctor may wish to study the patient’s vital metrics in comparison to those of their peer group. They can also create custom calculations and metrics, and build new data visualizations.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52