modelStudio: Interactive Studio with Explanations for ML Predictive Models

Machine learning predictive models are widely used in many areas of business and research. Their rising popularity is due to them being effective but often leads to problems with explaining their prediction. This has led to development of many Interpretable Machine Learning tools, e.g., DALEX (Biecek, 2018) R package, lime (Ribeiro, Singh, & Guestrin, 2016) and shap (Lundberg & Lee, 2017) Python packages and H2o.ai Driverless AI (Hall, Gill, Kurka, & Phan, 2017). Nowadays, we can see a huge demand for automation in many areas. This is how Automated Machine Learning and Automated Exploratory Data Analysis came to existence. AutoML (Truong et al., 2019) and AutoEDA (Staniak & Biecek, 2018) tools not only speed up the model development process but also often lead to new discoveries or higher quality of models. Explaining predictive models might be a time consuming and tedious task. Libraries for interpretable machine learning (Biecek, 2018; Carme, 2019; Jenkins, Nori, Koch, & Caruana, 2019; Meudec, 2019; Molnar, Casalicchio, & Bischl, 2018) require high programing skills and endless exploration of different aspects of a predictive model. There are tools for automation of the XAI process like modelDown (Romaszko, Tatarynowicz, Urbański, & Biecek, 2019) which produces static HTML site to compare and explain various models. Unfortunately, such tools are focused on global level explanations and deliver monotonous experience.