If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
You should always set the seed before calling train. Probably not the most amazing \(R 2\) value you have ever seen, but that's alright. Note that calling the model fit displays the most crucial information in a succinct way. Let's move on to a classification algorithm. It's good practice to start with a logistic regression and take it from there.
Ability for accurate hospital case cost modelling and prediction is critical for efficient health care financial management and budgetary planning. A variety of regression machine learning algorithms are known to be effective for health care cost predictions. The purpose of this experiment was to build an Azure Machine Learning Studio tool for rapid assessment of multiple types of regression models. The tool offers environment for comparing 14 types of regression models in a unified experiment: linear regression, Bayesian linear regression, decision forest regression, boosted decision tree regression, neural network regression, Poisson regression, Gaussian processes for regression, gradient boosted machine, nonlinear least squares regression, projection pursuit regression, random forest regression, robust regression, robust regression with mm-type estimators, support vector regression. The tool presents assessment results arranged by model accuracy in a single table using five performance metrics. Evaluation of regression machine learning models for performing hospital case cost prediction demonstrated advantage of robust regression model, boosted decision tree regression and decision forest regression. The operational tool has been published to the web and openly available for experiments and extensions.
Deep learning research in medicine is a bit like the Wild West at the moment; sometimes you find gold, sometimes a giant steampunk spider-bot causes a ruckus. This has derailed my series on whether AI will be replacing doctors soon, as I have felt the need to focus a bit more on how to assess the quality of medical AI research.
Binary classification is one of the most frequent studies in applied machine learning problems in various domains, from medicine to biology to meteorology to malware analysis. Many researchers use some performance metrics in their classification studies to report their success. However, the literature has shown a widespread confusion about the terminology and ignorance of the fundamental aspects behind metrics. In our paper tittled "Binary Classification Performance Measures/Metrics: A Comprehensive Visualized Roadmap to Gain New Insights" clarifies the confusing terminology, suggests formal rules to distinguish between measures and metrics for the first time, and proposes a new comprehensive visualized roadmap in a leveled structure for 22 measures and 22 metrics for exploring binary classification performance. Additionally, we introduced novel concepts such as canonical notation, duality, and complementation for measures/metrics, and suggested two new canonical base measures simplifying equations.
Competitive multi-player game play is a common feature in major commercial titles, and has formed the foundation for esports. In this paper, the question whether it is possible to predict match outcomes in First Person Shooter-type multi-player competitive games with mixed genres is addressed.The case employed is Destiny, which forms a hybrid title combining Massively Multi-player Online Role-Playing game features and First-Person Shooter games. Destiny provides the opportunity to investigate prediction of the match outcome, as well as the influence of performance metrics on the match results in a hybrid multi-player major commercial title. Two groups of models are presented for predicting match results: One group predicts match results for each individual game mode and the other group predicts match results in general, without considering specific game modes. Models achieve a performance between 63% and 99%in terms of average precision, with a higher performance recorded for the models trained on specific multi-player game modes, of which Destiny has several. We also analyzed performance metrics and their influence for each model. The results show that many key shooter performance metrics such as Kill/Death ratio are relevant across game modes, but also that some performance metrics are mainly important for specific competitive game modes. The results indicate that reliable match prediction is possible in FPS-type esports games.
Anyone that ever had to train a machine learning model had to go through some parameter sweeping (a.k.a. For random forests the parameters in need of optimization could be the number of trees in the model and the number of features considered at each split, for a neural network, there is the learning rate, the number of hidden layers, the number of hidden units in each layer, and several other parameters. Hyper-parameter optimization requires the use (and maybe the abuse) of a validation set on which you can't trust your performance metrics anymore. In this sense it is like a second phase of learning, or an extension to the learning algorithm itself. The performance metric (or the objective function) can be visualized as a heat-map in the n-dimensional parameter-space or as a surface in an n 1-dimensional space (the dimension n 1 being the value of that objective function).
These include: true positives, false positives (type 1 error), true negatives, and false negatives (type 2 error). There are many metrics for determining model performance for regression problems, but the most commonly used metric is known as the mean square error (MSE), or variation called the root mean square error (RMSE), which is calculated by taking the square root of the mean squared error. Recall the different results from a binary classifier, which are true positives, true negatives, false positives, and false negatives. Precision (positive predictive value) is the ratio of true positives to the total amount of positive predictions made (i.e., true or false).
Welcome to the fourth article in a five-part series about machine learning. In this article, we will take a deeper dive into model evaluation and performance metrics, and potential prediction-related errors that one may encounter. Before digging deeper into model performance and error types, we must first discuss the concept of residuals and errors for regression, positive and negative classifications for classification problems, and in-sample versus out-of-sample measurements. Any reference to models, metrics, or errors computed with respect to the data used to train, validate, or tune a predictive model (i.e., data you have) is called in-sample. Conversely, reference to test data metrics and errors, or new data in general is called out-of-sample (i.e., data you don't have).