Domino Data Lab

OCTOBER 7, 2020

Statistical methods for analyzing this two-dimensional data exist. MANOVA, for example, can test if the heights and weights in boys and girls is different. This statistical test is correct because the data are (presumably) bivariate normal. Each property is discussed below with R code so the reader can test it themselves.

Statistics 145

Statistics Testing Predictive Modeling Modeling 145

Smart Data Collective

AUGUST 20, 2021

You need to know how to leverage machine learning algorithms appropriately. Machine Learning Technology Can Be Ideal for Better Monetizing Your Android Apps. The statistic shows that users routinely open 4-6 applications every day. You need to know how to leverage your machine learning algorithms effectively.

Machine Learning 89

Machine Learning Data mining Advertising Deep Learning 89

O'Reilly on Data

DECEMBER 12, 2019

In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. Because ML models can react in very surprising ways to data they’ve never seen before, it’s safest to test all of your ML models with sensitivity analysis. [9] What can you do? Data augmentation.

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

IBM Big Data Hub

APRIL 18, 2024

LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language. Building an in-house team with AI, deep learning , machine learning (ML) and data science skills is a strategic move. What are the types of AGI?

Cost-Benefit 95

Cost-Benefit Modeling Manufacturing Interactive 95

DataRobot Blog

MAY 26, 2022

In this post, we will dive deeper into how members from both the first and second line of defense within a financial institution can adapt their model validation strategies in the context of modern ML methods. This may be accomplished through a wide variety of tests, to develop a deeper introspection into how the model behaves.

Risk 52

Risk Modeling Metrics Business Objectives 52

Domino Data Lab

DECEMBER 1, 2019

a change in the interest rate by a regulator, a natural calamity, the introduction of a new competitor in the market or a change in a business strategy/process, etc.). Step 4: Generate the test, train and noisy MNIST data sets. Detecting image drift. x_test = x_test.astype('float32') / 255. x_test_noisy = np.clip(x_test_noisy, 0.,

Modeling 77

Modeling Machine Learning Deep Learning Testing 77

O'Reilly on Data

MARCH 31, 2020

This means that the AI products you build align with your existing business plans and strategies (or that your products are driving change in those plans and strategies), that they are delivering value to the business, and that they are delivered on time. We won’t go into the mathematics or engineering of modern machine learning here.

Management 332

Management Machine Learning Experimentation Metrics 332

Domino Data Lab

APRIL 21, 2021

In contrast, the decision tree classifies observations based on attribute splits learned from the statistical properties of the training data. Machine Learning-based detection – using statistical learning is another approach that is gaining popularity, mostly because it is less laborious. describe().

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Domino Data Lab

AUGUST 10, 2021

This change makes models the new currency of competitive advantage, strategy, and growth. Some popular tool libraries and frameworks are: Scikit-Learn: used for machine learning and statistical modeling techniques including classification, regression, clustering and dimensionality reduction and predictive data analysis.

Modeling 64

Modeling IT Risk Management Deep Learning 64

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

On the one hand, basic statistical models (e.g. On the other hand, sophisticated machine learning models are flexible in their form but not easy to control. Curiosities and anomalies in your training and testing data become genuine and sustained loss patterns. linear regression, trees) can be too rigid in their functional forms.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

Smarten

APRIL 19, 2024

Assisted Predictive Modeling and Auto Insights to create predictive models using self-guiding UI wizard and auto-recommendations The Future of AI in Analytics The C=suite executive survey revealed that 93% felt that data strategy is critical to getting value from generative AI, but a full 57% had made no changes to their data.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

Domino Data Lab

JUNE 23, 2019

Be aware that machine learning often involves working on something that isn’t guaranteed to work. As a result, Skomoroch advocates getting “designers and data scientists, machine learning folks together and using real data and prototyping and testing” as quickly as possible. Testing is critical. It is similar to R&D.

Management 110

Management Machine Learning Experimentation Metrics 110

Domino Data Lab

MARCH 3, 2019

I’m here mostly to provide McLuhan quotes and test the patience of our copy editors with hella Californian colloquialisms. The data types used in deep learning are interesting. The data types used in deep learning are interesting. That represents a significant shift in strategy and cloud providers stand to benefit.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Domino Data Lab

JULY 9, 2019

A “data scientist” might build a multistage processing pipeline in Python, design a hypothesis test, perform a regression analysis over data samples with R, design and implement an algorithm in Hadoop, or communicate the results of our analyses to other members of the organization in a clear and concise fashion. .”

Data Science 112

Data Science Machine Learning Advertising Modeling 112

O'Reilly on Data

JANUARY 25, 2021

Both SRE and DevOps emphasize similar practices: version control (62% growth for GitHub, and 48% for Git), testing (high usage, though no year-over-year growth), continuous deployment (down 20%), monitoring (up 9%), and observability (up 128%). It’s particularly difficult if testing includes issues like fairness and bias.

Machine Learning 363

Machine Learning Software Testing Technology 363