O'Reilly on Data

DECEMBER 12, 2019

Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1] 1] This includes C-suite executives, front-line data scientists, and risk, legal, and compliance personnel. Model debugging is an emergent discipline focused on finding and fixing problems in ML systems.

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

IBM Big Data Hub

JULY 6, 2023

Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. Ultimately, data science is used in defining new business problems that machine learning techniques and statistical analysis can then help solve.

Machine Learning 58

Machine Learning Data Science Statistics Deep Learning 58

Smarten

AUGUST 21, 2019

Assisted Predictive Modeling – These tools enable the average business user to leverage sophisticated predictive algorithms without requiring statistical or data science skills. Users can highlight trends and patterns, test hypotheses and theories to reduce business risk, and easily predict and forecast results.

Analytics 51

Analytics Predictive Modeling Visualization Statistics 51

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

Business Over Broadway

SEPTEMBER 27, 2018

pharmacogenomics) and risk assessment of genetic disorders (e.g., genetic counseling, genetic testing). Analytics applied to these types of data help you generate better predictive models because your integrated data contain all the key variables that are useful in predicting customer churn. Summary.

Machine Learning 76

Machine Learning Data Integration Marketing Predictive Modeling 76

Birst BI

OCTOBER 30, 2018

A personal crystal ball that predicts your days ahead is what financial services firms everywhere want. Every day, these companies pose questions such as: Will this new client provide a good return on investment, relative to the potential risk? Is this existing client a termination risk? Will this next trade return a profit?

Predictive Analytics 73

Predictive Analytics Descriptive Analytics Analytics Metrics 73

Smarten

JUNE 27, 2018

For example, if an outlier indicates a risk or a mistake, that outlier should be identified and the risk or mistake should be addressed. All of these tools are designed for business users with average skills and require no special skills or knowledge of statistical analysis or support from IT or data scientists.

IT 40

IT Business Intelligence Predictive Modeling Testing 40

Smarten

JUNE 28, 2018

Business Problem: A bank wants to group loan applicants into high/medium/low risk based on attributes such as loan amount, monthly installments, employment tenure, the number of times the applicant has been delinquent in other payments, annual income, debt to income ratio etc. Use Case – 1. Use Case – 2.

IT 40

IT Business Intelligence Predictive Modeling Risk 40

Smarten

JUNE 25, 2018

Loan applicants in a bank might be grouped as low, medium, and high risk applicants based on applicant age, annual income, employment tenure, loan amount, the number of times a payment is delinquent etc. About Smarten.

Enterprise 40

Enterprise IT Business Intelligence Predictive Modeling 40

Sisense

SEPTEMBER 25, 2020

A data scientist must be skilled in many arts: math and statistics, computer science, and domain knowledge. Statistics and programming go hand in hand. Mastering statistical techniques and knowing how to implement them via a programming language are essential building blocks for advanced analytics. Linear regression.

Statistics 98

Statistics Predictive Modeling Modeling Machine Learning 98

Smarten

JUNE 29, 2018

Based on these labels, the bank can easily make a decision on whether to give a loan to an applicant and the credit limit and interest rate each applicant is eligible for based on the amount of risk involved. Use Case – 2.

IT 40

IT Business Intelligence Predictive Modeling Statistics 40

Smarten

JUNE 27, 2018

Based on these labels, the bank can easily make a decision on whether to give loan to an applicant and how much credit to extend, as well as the interest rate each applicant is eligible for based on the amount of risk involved. Use Case – 2.

Business Analytics 40

Business Analytics Analytics IT Business Intelligence 40

Smarten

JUNE 29, 2018

Based on these labels, the bank can easily make a decision on whether to give a loan to an applicant and the credit limit and interest rate for each applicant, based on the amount of risk. Use Case – 2.

Enterprise 40

Enterprise Business Intelligence Predictive Modeling Statistics 40

Smarten

JUNE 27, 2018

In this way, the riskier customers are identified easily and bank can avert the risk of delinquencies. Business Problem: Based on customer attributes and past online shopping behavioral data, an online retail giant wants to predict the future purchases of customers. Use Case – 2.

IT 40

IT Business Intelligence Predictive Modeling Statistics 40

Insight

FEBRUARY 19, 2020

Using variability in machine learning predictions as a proxy for risk can help studio executives and producers decide whether or not to green light a film project Photo by Kyle Smith on Unsplash Originally posted on Toward Data Science. I held out 20% of this as a test set and used the remainder for training and validation.

Risk 67

Risk ROI Modeling Metrics 67

Domino Data Lab

APRIL 21, 2021

Rules-based fraud detection (top) vs. classification decision tree-based detection (bottom): The risk scoring in the former model is calculated using policy-based, manually crafted rules and their corresponding weights. Let’s also look at the basic descriptive statistics for all attributes. 3f" % x) dataDF.describe().

Statistics 69

Statistics Machine Learning Modeling Metrics 69

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

Occam's Razor

NOVEMBER 17, 2008

There is a tendency to think experimentation and testing is optional. Just don't fall for their bashing of all other vendors or their silly claims, false, of "superiority" in terms of running 19 billion combinations of tests or the bonus feature of helping you into your underwear each morning. 4 Big Bets, Low Risks, Happy Customers.

Testing 112

Testing Experimentation Risk Optimization 112

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

Smarten

JULY 10, 2018

Independent Samples T Test: What is the Independent Samples T Test Method of Analysis and How Can it Benefit an Organization? Use Case(s): Predict if loan default based on attributes of applicant; predict likelihood of successful treatment of new patient based on patient attributes and more.

Analytics 40

Analytics Forecasting Statistics Sales 40

Smarten

JUNE 25, 2018

This article provides a brief explanation of the definition and uses of the Descriptive Statistics algorithms. What is a Descriptive Statistics? Descriptive statistics helps users to describe and understand the features of a specific dataset, by providing short summaries and a graphic depiction of the measured data.

Statistics 40

Statistics Enterprise Measurement Business Intelligence 40

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

datapine

NOVEMBER 27, 2019

This is one of the major trends chosen by Gartner in their 2020 Strategic Technology Trends report , combining AI with autonomous things and hyperautomation, and concentrating on the level of security in which AI risks of developing vulnerable points of attacks. Industries harness predictive analytics in different ways.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

O'Reilly on Data

MARCH 24, 2020

John Myles White , data scientist and engineering manager at Facebook, wrote: “The biggest risk I see with data science projects is that analyzing data per se is generally a bad thing. So when you’re missing data or have “low-quality data,” you use assumptions, statistics, and inference to repair your data.

Machine Learning 346

Machine Learning Statistics Data Quality Modeling 346

Jet Global

MAY 1, 2023

CEO Priorities Grow revenue and “hit the number” Manage costs and meet profitability goals Attract and retain talent Innovate and out-perform the competition Manage risk Connect the Dots Present embedded analytics as a way to differentiate from the competition and increase revenue. Present your business case.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52