Robert E. Hoyt David Patrishkoff Introduction Synthetic data is artificial data that is generated with AI and other modalities when not enough real world data is available to sufficiently train a predictive model or privacy is an issue. Synthetic data is only intended to expand the training dataset and is…

Introduction We live in an era in which machine learning and artificial intelligence can generate a myriad of predictions. One difficulty, however, is can we explain how the model came to its conclusions? Can we explain this to non-data scientists? Is it scientifically correct? …

Are Imbalanced Datasets Common and Challenging? A major challenge in several fields such as fraud detection and medicine is the fact that the category/class/outcome you are interested in is often a small minority. For example, financial fraud constitutes about 3% of inquiries which means that 97% are not fraudulent. Similarly…

In part 1 of this series, I provided an overview of the data mining platform Orange which focuses on data science education. In part 2 I will focus on some unique Orange educational features users should find helpful. …

It’s about time a web database system was designed for usability Finding a relational database for business or education sounds easy but, in many instances, it is not. There may be connection, configuration, or installation challenges creating a new database. Charges for use may be difficult to understand. …

I enjoyed reading your series of articles on Orange. My question is how to reduce data leakage. It is clear how to split the data into train/test with Data Sampler but what if you want to impute, normalize the data or pick the top 5 relevant predictors? I get the impression that you connect the Preprocess widget directly to Test and Score. It also receives the train and test data from the Data Sampler widget. Do you believe this is the correct workflow to minimize data leakage? Thanks

You mention missing data but I didn't see any code to find missing values, just null values. This dataset is riddled with missing data. An insulin level of zero or triceps thickness of zero means it wasn't done as opposed to an actual value of zero. By the same token, if zero is listed for pregnancies, does that mean it was not asked or the woman has never been pregnant? Nobody understands the diabetic pedigree column. Personally, as a physician data scientist I don't use this dataset for those reasons

In the past few years, artificial intelligence (AI) has become synonymous with deep learning which is based on artificial neural networks (ANNs) with multiple layers. In healthcare, the most common use of AI is image recognition, particularly for the fields of cardiology, pathology, radiology, and ophthalmology. …

It is difficult to find patient-level data of sufficient size for research, modeling, or software development. This is largely due to HIPAA concerns and the overall lack of interoperability in the US healthcare system. Synthetic data has potential in those areas but much of the generated data is non-medical. For…