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 321

Machine Learning Predictive Modeling Software Modeling 321

Sisense

SEPTEMBER 25, 2020

No matter your skill, career level, or title, the ability to analyze, organize, and visualize data are vital skills in our world of quickly growing and ever-changing data. Linear regression is a form of supervised learning (or predictive modeling). We have predicted review text with 91.27% accuracy via logistic regression.

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 75

Machine Learning Modeling Deep Learning Predictive Modeling 75

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. Built-in Code Snippets and Interactive Visualizations to Accelerate Experimentation. Did you notice?

Experimentation 52

Experimentation Machine Learning Data Science Modeling 52

DataRobot Blog

DECEMBER 1, 2022

For example, you could set up a data pipeline that delivers DataRobot predictions to HubSpot to automatically initiate offers within the business rules you set. You could also use the predictions to visualize a BI dashboard or report for your marketing managers to access. Training and Testing Different AI Models.

Machine Learning 52

Machine Learning Marketing Modeling Statistics 52

Occam's Razor

NOVEMBER 18, 2014

Like a vast majority on planet Earth, I love data visualizations. A day-to-day manifestation of this love is on my Google+ or Facebook profiles where 75% of my posts are related to my quick analysis and learnings from a visualization. Data visualized is data understood. But for a visual person like me, this is the ah-ha moment.

Visualization 143

Visualization Slice and Dice Predictive Modeling Metrics 143

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 11.9

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. R has yet one more way to visualize these relationships in multiple dimensions.

Statistics 59

Statistics Machine Learning Testing Modeling 59

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 286

Data Science Forecasting Business Intelligence Sales 286

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

Spreadsheets finally took a backseat to actionable and insightful data visualizations and interactive business dashboards. Companies are no longer wondering if data visualizations improve analyses but what is the best way to tell each data-story. 2) Data Discovery/Visualization. Data exploded and became big.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

Jet Global

MAY 1, 2023

Plus, there is an expectation that tools be visually appealing to boot. In the past, data visualizations were a powerful way to differentiate a software application. Their dashboards were visually stunning. Today, free visualizations seem to be everywhere. Users’ varied needs require a shift in traditional BI thinking.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52