By Trevor Hastie, Robert Tibshirani, Jerome Friedman
Prior to now decade there was an explosion in computation and knowledge know-how. With it have come giant quantities of information in various fields reminiscent of drugs, biology, finance, and advertising. The problem of figuring out those information has ended in the improvement of recent instruments within the box of information, and spawned new parts similar to info mining, computing device studying, and bioinformatics. lots of those instruments have universal underpinnings yet are usually expressed with various terminology. This publication describes the real principles in those parts in a standard conceptual framework. whereas the strategy is statistical, the emphasis is on ideas instead of arithmetic. Many examples are given, with a liberal use of colour portraits. it may be a invaluable source for statisticians and an individual attracted to info mining in technology or undefined. The book's assurance is vast, from supervised studying (prediction) to unsupervised studying. the various themes contain neural networks, help vector machines, class timber and boosting---the first accomplished remedy of this subject in any booklet. This significant re-creation positive factors many issues no longer coated within the unique, together with graphical versions, random forests, ensemble tools, least attitude regression & course algorithms for the lasso, non-negative matrix factorization, and spectral clustering. there's additionally a bankruptcy on tools for wide'' facts (p greater than n), together with a number of checking out and fake discovery rates.
Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of information at Stanford collage. they're trendy researchers during this sector: Hastie and Tibshirani built generalized additive versions and wrote apopular publication of that identify. Hastie co-developed a lot of the statistical modeling software program and atmosphere in R/S-PLUS and invented valuable curves and surfaces. Tibshirani proposed the lasso and is co-author of the very winning An advent to the Bootstrap. Friedman is the co-inventor of many data-mining instruments together with CART, MARS, projection pursuit and gradient boosting.
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Additional info for The Elements of Statistical Learning: Data Mining, Inference, and Prediction
A has a bigger chance of winning. You may need to play the game to find this out. ” You can find the chance for rolling a double with two number cubes from the chart in Section A. So the chance of doubles is less than the chance of “not doubles,” which means that A (who wins if no double occurs) has a bigger chance of winning. 2 a. If you roll a number cube a thousand times, you would expect 1 of the rolls, so about 1000 —— = 167 times. a 6 to come up in about ᎑᎑ 6 6 b. The numbers divisible by three on a number cube are 3 and 6, which is one third of the numbers on the number cube.
These are the results: Number of Questions out of Five Correct Number of Times This Occurred 0 9 1 16 2 13 3 5 4 7 5 0 c. Based on these results, what is the chance that Peter will get three or more questions on the quiz correct by guessing? Think of a situation different from the ones in this section where you would have to simulate the situation many times to estimate the chance of an outcome. Think of another situation where you could figure out the theoretical chance and would not need to simulate the situation.
Write the chance as a fraction and as a percent. b. What is the chance, as a percentage, of rolling “not 1s” with six number cubes in the game of Hog? 2. Mr. and Mrs. Lewis have four daughters. You may assume that the chance of having a son is the same as having a daughter: 1᎑᎑2 . Comment on each of the following statements. a. The chance that their next child is a girl is smaller than 1᎑᎑2 because a family with five daughters is very unlikely. b. The chance is one half because the chance of a girl is 1᎑᎑2 .