Causal inference is central to many areas of artificial intelligence, including complex reasoning, planning, knowledge-base construction, robotics, explanation, and fairness. An active community of researchers develops and enhances algorithms that learn causal models from data, and this work has produced a series of impressive technical advances. However, evaluation techniques for causal modeling algorithms have remained somewhat primitive, limiting what we can learn from experimental studies of algorithm performance, constraining the types of algorithms and model representations that researchers consider, and creating a gap between theory and practice. We argue for more frequent use of evaluation techniques that examine interventional measures rather than structural or observational measures, and that evaluate those measures on empirical data rather than synthetic data. We survey the current practice in evaluation and show that these are rarely used in practice. We show that such techniques are feasible and that data sets are available to conduct such evaluations. We also show that these techniques produce substantially different results than using structural measures and synthetic data.

As of June 2019, more than 30 artificial intelligence (AI) algorithms have been approved by the US Food and Drug Administration (including those for the detection of diabetic retinopathy, stroke, brain hemorrhage and atrial fibrillation)1 and over 300 clinical trials have been registered at ClinicalTrials.gov These algorithms have the potential to transform healthcare, by offering earlier and more accurate diagnoses, providing novel insights for the understanding of diseases, enabling faster and more efficient service delivery and making medical care more available to those who really need it. Optimal reporting is key for evaluating the clinical utility of algorithms, for informing health policy and evidence-based recommendations and for preventing research waste2. Most AI interventions thus far, particularly diagnostic algorithms, have been evaluated only in the context of diagnostic accuracy. Although this initial validation stage is important, a demonstration of good diagnostic accuracy does not necessarily translate to improved patient outcomes.

Gini impurity is a measure of how often a randomly chosen element from the set would be incorrectly labeled if it was randomly labeled according to the distribution of labels in the subset. In simple terms, Gini impurity is the measure of impurity in a node. So to understand the formula a little better, let us talk specifically about the binary case where we have nodes with only two classes. So in the below five examples of candidate nodes labelled A-E and with the distribution of positive and negative class shown, which is the ideal condition to be in? I reckon you would say A or E and you are right.

Bagging (or Bootstrap Aggregation) is used when the goal is to reduce the variance of a DT. Variance relates to the fact that DTs can be quite unstable because small variations in the data might result in a completely different Tree being generated. So, the idea of Bagging is to solve this issue by creating in parallel random subsets of data (from the training data), where any observation has the same probability to appear in a new subset data. Next, each collection of subset data is used to train DTs, resulting in an ensemble of different DTs. Finally, an average of all predictions of those different DTs is used, which produces a more robust performance than single DTs.

In this article, we will understand decision tree by implementing an example in Python using the Sklearn package (Scikit Learn).

In this article, we will understand decision tree by implementing an example in Python using the Sklearn package (Scikit Learn). Let's first discuss what is a decision tree. A decision tree has two components, one is the root and other is branches. The root represents the problem statement and the branches represent the solutions or consequences.Initially the problem or the root is split into two branches or consequences, and from the branches again a split occurs and further branches are created. In this article we will discuss about regression trees.

Upper gastrointestinal cancers (including oesophageal cancer and gastric cancer) are the most common cancers worldwide. Artificial intelligence platforms using deep learning algorithms have made remarkable progress in medical imaging but their application in upper gastrointestinal cancers has been limited. We aimed to develop and validate the Gastrointestinal Artificial Intelligence Diagnostic System (GRAIDS) for the diagnosis of upper gastrointestinal cancers through analysis of imaging data from clinical endoscopies.

Use expert visualization or conventional clinical indices can lack accuracy for borderline classications. Advanced statistical approaches based on eigen-decomposition have been mostly concerned with shape and motion indices. In this paper, we present a new approach to identify CVDs from cine-MRI by estimating large pools of radiomic features (statistical, shape and textural features) encoding relevant changes in anatomical and image characteristics due to CVDs. The calculated cine-MRI radiomic features are assessed using sequential forward feature selection to identify the most relevant ones for given CVD classes (e.g. myocardial infarction, cardiomyopathy, abnormal right ventricle). Finally, advanced machine learning is applied to suitably integrate the selected radiomics for final multi-feature classification based on Support Vector Machines (SVMs). The proposed technique was trained and cross-validated using 100 cine-MRI cases corresponding to five different cardiac classes from the ACDC MICCAI 2017 challenge \footnote{https://www.creatis.insa-lyon.fr/Challenge/acdc/index.html}. All cases were correctly classified in this preliminary study, indicating potential of using large-scale radiomics for MRI-based diagnosis of CVDs.

Concept drift is formally defined as the change in joint distribution of a set of input variables X and a target variable y. The two types of drift that are extensively studied are real drift and virtual drift where the former is the change in posterior probabilities p(y|X) while the latter is the change in distribution of X without affecting the posterior probabilities. Many approaches on concept drift detection either assume full availability of data labels, y or handle only the virtual drift. In a streaming environment, the assumption of full availability of data labels, y is questioned. On the other hand, approaches that deal with virtual drift failed to address real drift. Rather than improving the state-of-the-art methods, this paper presents a semi-supervised framework to deal with the challenges above. The objective of the proposed framework is to learn from streaming environment with limited data labels, y and detect real drift concurrently. This paper proposes a novel concept drift detection method utilizing the densities of posterior probabilities in partially labeled streaming environments. Experimental results on both synthetic and realworld datasets show that our proposed semi-supervised framework enables the detection of concept drift in such environment while achieving comparable prediction performance to the state-of-the-art methods.

I am going to talk about the details of four outlier detection methodologies implemented in R Studio. I will be mentioning what the outlier is, why it is important and why outliers occur. To put simply, an outlier is a data point that differs greatly (much smaller or larger than) from other values in a dataset. Outliers may be because of random variation or may demonstrate something scientifically interesting. In any event, we should not simply eliminate the outlying observation before a careful investigation.