Depending on the algorithm/model that generates this dataset metrics present in the dataset will vary. Here is a list of metrics based on the model: Linear Regression, CART numeric, Elastic Net Linear: R-Square, R-Square Adjusted, Mean Absolute Error(MAE), Mean Squared Error(MSE), Relative Absolute Error(RAE), Related Squared Error(RSE), Root Mean Squared Error(RMSE) CART(Classification And Regression Trees), Naive Bayes Classification, Neural Network, Support Vector Machine(SVM), Random Forest, Logistic Regression: Now you know what the Related datasets are and how they can be useful for fine tuning your Machine Learning model or for comparing two different models. .
With the growing adoption of machine learning techniques, there is a surge of research interest towards making machine learning systems more transparent and interpretable. Various visualizations have been developed to help model developers understand, diagnose, and refine machine learning models. However, a large number of potential but neglected users are the domain experts with little knowledge of machine learning but are expected to work with machine learning systems. In this paper, we present an interactive visualization technique to help users with little expertise in machine learning to understand, explore and validate predictive models. By viewing the model as a black box, we extract a standardized rule-based knowledge representation from its input-output behavior. We design RuleMatrix, a matrix-based visualization of rules to help users navigate and verify the rules and the black-box model. We evaluate the effectiveness of RuleMatrix via two use cases and a usability study.
But it is not a cake walk to analyze it as greater things come at a greater cost. With the exponential growth in data, there requires a process to extract meaningful information as conclude to useful insights. Data mining is the process where the discovery of patterns among large sets of data to transform it into effective information is performed. This technique utilizes specific algorithms, statistical analysis, artificial intelligence and database systems to juice out the information from huge datasets and convert them into an understandable form. This article lists out 10 comprehensive data mining tools widely used in the big data industry.
Healthcare organizations need predictive analytics for providing quality healthcare and population health management. Building predictive models by applying machine learning algorithms is complex in the infrastructure-as-a-service or platform-as-as-a-service environment as it involves distributed computing. The emergence of predictive analytics in the healthcare industry has offered enormous opportunity to be able to predict the events in healthcare organization and other industries as well such as aerospace industry. Predictive analytics is a subfield of data science that deploys several multi-disciplinary fields such as statistical inference, machine learning, clustering, data visualization, and machine learning iteratively through the lifecycle of the data analytics. The stages can be defined as defining the problem statement for the organization, scope of the data analytics project, collection of big data, exploratory data analysis, data preparation, deployment of predictive models leveraging machine learning algorithms.
As machine learning becomes more pervasive, there is an urgent need for interpretable explanations of predictive models. Prior work has developed effective methods for visualizing global model behavior, as well as generating local (instance-specific) explanations. However, relatively little work has addressed regional explanations - how groups of similar instances behave in a complex model, and the related issue of visualizing statistical feature interactions. The lack of utilities available for these analytical needs hinders the development of models that are mission-critical, transparent, and align with social goals. We present VINE (Visual INteraction Effects), a novel algorithm to extract and visualize statistical interaction effects in black box models. We also present a novel evaluation metric for visualizations in the interpretable ML space.