Analytics Vidhya
NOVEMBER 23, 2021
Introduction to Classification Algorithms In this article, we shall analyze loan risk using 2 different supervised learning classification algorithms. The post Loan Risk Analysis with Supervised Machine Learning Classification appeared first on Analytics Vidhya.
O'Reilly on Data
NOVEMBER 13, 2018
As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations. We recently conducted a survey which garnered more than 11,000 respondents—our main goal was to ascertain how enterprises were using machine learning. Let’s begin by looking at the state of adoption.
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Analytics Vidhya
APRIL 1, 2022
Borrowers who default on loans not only damage their credit but also risk being sued […]. The post Predicting Possible Loan Default Using Machine Learning appeared first on Analytics Vidhya. Introduction A loan default occurs when a borrower takes money from a bank and does not repay the loan.
Analytics Vidhya
DECEMBER 13, 2023
Deterministic and stochastic models are approaches in various fields, including machine learning and risk assessment. This article will explore the pros and cons of deterministic and stochastic models, their applications, and their impact on machine learning and risk assessment.
Advertiser: Data Robot
Estimating the risks or rewards of making a particular loan, for example, has traditionally fallen under the purview of bankers with deep knowledge of the industry and extensive expertise. By leveraging the power of automated machine learning, banks have the potential to make data-driven decisions for products, services, and operations.
O'Reilly on Data
DECEMBER 12, 2019
For all the excitement about machine learning (ML), there are serious impediments to its widespread adoption. 1] This includes C-suite executives, front-line data scientists, and risk, legal, and compliance personnel. Not least is the broadening realization that ML models can fail. That’s where model debugging comes in.
O'Reilly on Data
MAY 21, 2019
Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms. I will highlight the results of a recent survey on machine learning adoption, and along the way describe recent trends in data and machine learning (ML) within companies.
Advertiser: Data Robot
The risk of bias in artificial intelligence (AI) has been the source of much concern and debate. These risks undermine the underlying trust in AI and affect your organization’s ability to deliver successful AI projects, unhindered by potential ethical and reputational consequences.
Advertiser: Data Robot
Estimating the risks or rewards of making a particular loan, for example, has traditionally fallen under the purview of bankers with deep knowledge of the industry and extensive expertise. By leveraging the power of automated machine learning, banks have the potential to make data-driven decisions for products, services, and operations.
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