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. After cleaning, the data is now ready for processing.

Data Science 110

Data Science Statistics Recreation/Entertainment Predictive Analytics 110

O'Reilly on Data

DECEMBER 12, 2019

More structured approaches to sensitivity analysis include: Adversarial example searches : this entails systematically searching for rows of data that evoke strange or striking responses from an ML model. Figure 1 illustrates an example adversarial search for an example credit default ML model. What can you do? Data augmentation.

Machine Learning 190

Machine Learning Modeling Testing Risk Management 190

Domino Data Lab

OCTOBER 7, 2020

Statistics developed in the last century are based on probability models (distributions). This approach provides an automatic and objective strategy for decision making under defined assumptions about the data. This model for data analytics has proven highly successful in basic biomedical research and clinical trials.

Statistics 145

Statistics Testing Predictive Modeling Modeling 145

IBM Big Data Hub

FEBRUARY 14, 2024

Promote cross- and up-selling Recommendation engines use consumer behavior data and AI algorithms to help discover data trends to be used in the development of more effective up-selling and cross-selling strategies, resulting in more useful add-on recommendations for customers during checkout for online retailers.

Cost-Benefit 86

Cost-Benefit Insurance Unstructured Data Machine Learning 86

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 221

Modeling Machine Learning Predictive Modeling Consulting 221

Domino Data Lab

APRIL 21, 2021

deep learning) there is no guaranteed explainability. We will go through a typical ML pipeline, where we do data ingestion, exploratory data analysis, feature engineering, model training and evaluation. There are different approaches to this strategy, with the two most commonly used being random oversampling and SMOTE.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

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

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 79

Machine Learning Modeling Deep Learning Predictive Modeling 79

Alation

APRIL 14, 2022

They strove to ramp up skills in all manner of predictive modeling, machine learning, AI, or even deep learning. According to Strategies in IT Governance , “[Data] governance is the system by which entities are directed and controlled. Data Governance and Data Security.

Metadata 59

Metadata Data Governance Modeling Data-driven 59