"Model" means different things to different people and different things at different times. As I briefly explain in A Model's Many Faces, I often find it helpful to classify models as conceptual, operational or statistical. In this post we'll have a closer look at the last of these, statistical models. First, it's critical to understand that statistical models are simplified representations of reality and, to paraphrase the famous words of statistician George Box, they're all wrong but some of them are useful. So why do we use statistical models?

Innovation diffusion has been studied extensively in a variety of disciplines, including sociology, economics, marketing, ecology, and computer science. Traditional literature on innovation diffusion has been dominated by models of aggregate behavior and trends. However, the agent-based modeling (ABM) paradigm is gaining popularity as it captures agent heterogeneity and enables fine-grained modeling of interactions mediated by social and geographic networks. While most ABM work on innovation diffusion is theoretical, empirically grounded models are increasingly important, particularly in guiding policy decisions. We present a critical review of empirically grounded agent-based models of innovation diffusion, developing a categorization of this research based on types of agent models as well as applications. By connecting the modeling methodologies in the fields of information and innovation diffusion, we suggest that the maximum likelihood estimation framework widely used in the former is a promising paradigm for calibration of agent-based models for innovation diffusion. Although many advances have been made to standardize ABM methodology, we identify four major issues in model calibration and validation, and suggest potential solutions.

Machine learning techniques are increasingly demonstrating success in image-based diagnosis, disease detection and disease prognosis. To reduce operator dependency and get better diagnostic accuracy, a computer aided diagnositic (CAD) system is a valuable and beneficial means for breast tumor detection and classification, fetal development and growth, Brain functioning, skin lesions and Lungs diseases [1]. Image denoising using machine learning techniques plays important role in the various application area of medical imaging such as pre-processing (noise removal from Ultrasound (US) images, segmentation (MRI of brain tumors and lung infections using X-rays), Computer aided diagnosis (CAD) for breast cancer, fetus development and many more). Further, denoising of medical images using data mining methods are analyzed. This paper focuses on the review of various denoising methods along with machine learning approaches to develop a systematic decision for diagnosing and prediction for medical images. The representation of the machine learning i.e. based on various numbers of methods which focuses on prediction, based on known properties learned from the training data has been considered.

What is a Time Series? Many data sets are cross-sectional and represent a single slice of time. However, we also have data collected over many periods - weekly sales data, for instance. This is an example of time series data. Time series analysis is a specialized branch of statistics used extensively in fields such as Econometrics and Operations Research.

The Artificial Intelligence Market Forecasts 2016 -2025 across 27 Industry Sectors has provided an overview of numerous Artificial Intelligence use cases, which includes Machine Learning, machine reasoning, Deep Learning, NLP, computer vision, and many other allied technologies. According to this study, food services, consumer products, advertising, and defense (along with others mentioned above) will significantly benefit from the growth of AI in the coming years.

Artificial Intelligence Set to Transform Insurance Industry but Integration Challenges Remain, According to Accenture Report Insurers are investing in AI technology to enhance the customer experience, empower their own people NEW YORK; Apr. 19, 2017 – Insurance executives believe that artificial intelligence (AI) will significantly transform their industry in the next three years, with insurers investing in AI to empower agents, brokers and employees to enhance the customer experience with automated personalized services, faster claims handling and individual risk-based underwriting processes, according to Accenture's Technology Vision for Insurance 2017. At the same time, however, the report found that insurers face challenges integrating AI into their existing technology, citing issues such as data quality, privacy and infrastructure compatibility. Titled "Technology for People," the report is based on the insights of a technology advisory board, interviews with industry technologists and a survey of more than 550 insurance executives across 31 countries. According to the report, three-quarters (75 percent) of insurance executives believe that AI will either significantly alter or completely transform the overall insurance industry in the next three years. One-third (32 percent) believe that their own company will be "completely transformed" by AI within that timeframe, and an additional 39 percent believe that AI will "significantly change" their company.

While leading the data science team at DataRPM, we have had the opportunity to interact with major players in the world for Discrete and Process Manufacturing. What we have found is that most companies (at-least those that matter) are moving towards an era of being resource consciousness since the cost of resources are going up. Companies in the past could focus on increasing top-line growth and comfortably lead the market. That is no longer true. Every CEO today has to worry about every line under the costs header and keep it under control.

A fair amount of our data science projects involve machine learning, and many parts of this workflow are repetitive. Model Diagnostics: Learning curves, partial dependence plots, feature importances, ROC and other diagnostics are extremely useful to generate automatically. AML is a powerful set of techniques for faster data exploration as well as improving model accuracy through model tuning and better diagnostics. The above case study highlights AML's capability to improve model accuracy, however we have realized AMLs other benefits as well.

A few weeks ago the new U.S. Treasury Secretary Steven Mnuchin took some public flak for suggesting, in response to an interviewer's question, that he was "not worried at all" that artificial intelligence would threaten the jobs of human workers, because in his view it is "50 or 100 years away." It's not clear why Mnuchin would say that, but with respect, I have to correct him. The original question to Mnuchin was rooted in popular worries that AI will eliminate jobs in the near future. However there's growing evidence that as companies embrace AI to stay competitive, which they will, in the end these changes will create more jobs than they destroy. Earlier this year, ServiceNow commissioned a survey of senior executives at 1,874 companies of varying sizes across numerous industries in seven global markets.

While this will serve as an overview of Scott's essay, which you can read for further detail and mathematical insights, we will start by with Fortmann-Roe's verbatim definitions which are central to the piece: Error due to Bias: The error due to bias is taken as the difference between the expected (or average) prediction of our model and the correct value which we are trying to predict. Again, imagine you can repeat the entire model building process multiple times. Fortmann-Roe ends the section on over- and under-fitting by pointing to another of his great essays (Accurately Measuring Model Prediction Error), and then moving on to the highly-agreeable recommendation that "resampling based measures such as cross-validation should be preferred over theoretical measures such as Aikake's Information Criteria." I recommend reading Scott Fortmann-Roe's entire bias-variance tradeoff essay, as well as his piece on measuring model prediction error.