DrinkA drink or beverage is a liquid intended for human consumption. In addition to their basic function of satisfying thirst, drinks play important roles in human culture. Common types of drinks include plain drinking water, milk, juice, smoothies and soft drinks. Traditionally warm beverages include coffee, tea, and hot chocolate. Caffeinated drinks that contain the stimulant caffeine have a long history. In addition, alcoholic drinks such as wine, beer, and liquor, which contain the drug ethanol, have been part of human culture for more than 8,000 years.
Social mediaSocial media are interactive technologies that facilitate the creation and sharing of information, ideas, interests, and other forms of expression through virtual communities and networks. While challenges to the definition of social media arise due to the variety of stand-alone and built-in social media services currently available, there are some common features: Social media are interactive Web 2.0 Internet-based applications.
Random forestRandom forests or random decision forests is an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time. For classification tasks, the output of the random forest is the class selected by most trees. For regression tasks, the mean or average prediction of the individual trees is returned. Random decision forests correct for decision trees' habit of overfitting to their training set.
Drunk drivingDrunk driving (or drink-driving in British English) is the act of driving under the influence of alcohol. A small increase in the blood alcohol content increases the relative risk of a motor vehicle crash. In the United States, alcohol is involved in 30% of all traffic fatalities. Short-term effects of alcohol consumption Alcohol has a very significant effect on the functions of the body which are vital to driving and being able to function. Alcohol is a depressant, which mainly affects the function of the brain.
Alcohol and cancerAlcohol causes cancers of the oesophagus, liver, breast, colon, oral cavity, rectum, pharynx, and larynx, and probably causes cancers of the pancreas. Consumption of alcohol in any quantity can cause cancer. The more alcohol is consumed, the higher the cancer risk, and no amount can be considered safe. Alcoholic beverages were classified as a Group 1 carcinogen by the International Agency for Research on Cancer (IARC) in 1988. 3.6% of all cancer cases and 3.
Sugary drink taxA sugary drink tax, soda tax, or sweetened beverage tax (SBT) is a tax or surcharge (food-related fiscal policy) designed to reduce consumption of sweetened beverages. Drinks covered under a soda tax often include carbonated soft drinks, sports drinks and energy drinks. This policy intervention is an effort to decrease obesity and the health impacts related to being overweight, however the medical evidence supporting the benefits of a sugar tax on health is of very low certainty.
Ensemble learningIn statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Unlike a statistical ensemble in statistical mechanics, which is usually infinite, a machine learning ensemble consists of only a concrete finite set of alternative models, but typically allows for much more flexible structure to exist among those alternatives.
Bootstrap aggregatingBootstrap aggregating, also called bagging (from bootstrap aggregating), is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression. It also reduces variance and helps to avoid overfitting. Although it is usually applied to decision tree methods, it can be used with any type of method. Bagging is a special case of the model averaging approach.
Boosting (machine learning)In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones. Boosting is based on the question posed by Kearns and Valiant (1988, 1989): "Can a set of weak learners create a single strong learner?" A weak learner is defined to be a classifier that is only slightly correlated with the true classification (it can label examples better than random guessing).
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.
