Naive Bayes classifierIn statistics, naive Bayes classifiers are a family of simple "probabilistic classifiers" based on applying Bayes' theorem with strong (naive) independence assumptions between the features (see Bayes classifier). They are among the simplest Bayesian network models, but coupled with kernel density estimation, they can achieve high accuracy levels. Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predictors) in a learning problem.
Linear classifierIn the field of machine learning, the goal of statistical classification is to use an object's characteristics to identify which class (or group) it belongs to. A linear classifier achieves this by making a classification decision based on the value of a linear combination of the characteristics. An object's characteristics are also known as feature values and are typically presented to the machine in a vector called a feature vector.
Foley catheterIn urology, a Foley catheter (named for Frederic Foley, who produced the original design in 1929) is a flexible tube that a clinician passes through the urethra and into the bladder to drain urine. It is the most common type of indwelling urinary catheter. The tube has two separated channels, or lumina (sg. lumen), running down its length. One lumen, open at both ends, drains urine into a collection bag. The other has a valve on the outside end and connects to a balloon at the inside tip.
WarfarinWarfarin is an anticoagulant used as a medication under several brand names including Coumadin. While the drug is described as a "blood thinner", it does not reduce viscosity but inhibits coagulation, and is commonly used to prevent blood clots in the circulatory system such as deep vein thrombosis and pulmonary embolism, and to protect against stroke in people who have atrial fibrillation, valvular heart disease, or artificial heart valves. Less commonly, it is used following ST-segment elevation myocardial infarction and orthopedic surgery.
Partition of sums of squaresThe partition of sums of squares is a concept that permeates much of inferential statistics and descriptive statistics. More properly, it is the partitioning of sums of squared deviations or errors. Mathematically, the sum of squared deviations is an unscaled, or unadjusted measure of dispersion (also called variability). When scaled for the number of degrees of freedom, it estimates the variance, or spread of the observations about their mean value.
