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ike every modern operating system, language, and programming tool of any importance, Mac OS X and the iPhone s operating system (also OS X) were built using the C programming language Not the Objective-C programming language and Cocoa framework, not the C++ programming language, but C Objective-C is an object-oriented language built using C Cocoa is a framework that hides difficult C programming tasks with easy-to-use objects programmed in Objective-C But behind every Cocoa object you construct, you find C code defining the construct

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(eg, errors(h)) for this quantity Because the estimator is a random variable (eg, errors(h) depends on the random sample S), it can be characterized by the probability distribution that governs its value Confidence intervals can then be calculated by determining the interval that contains the desired probability mass under this distribution One possible cause of errors in estimating hypothesis accuracy is estimation bias If Y is an estimator for some parameter p, the estimation bias of Y is the difference between p and the expected value of Y For example, if S is the training data used to formulate hypothesis h, then errors(h) gives an optimistically biased estimate of the true error errorD(h) A second cause of estimation error is variance in the estimate Even with an unbiased estimator, the observed value of the estimator is likely to vary from one experiment to another The variance a2 of the distribution governing the estimator characterizes how widely this estimate is likely to vary from the correct value This variance decreases as the size of the data sample is increased Comparing the relative effectiveness of two learning algorithms is an estimation problem that is relatively easy when data and time are unlimited, but more difficult when these resources are limited One possible approach described in this chapter is to run the learning algorithms on different subsets of the available data, testing the learned hypotheses on the remaining data, then averaging the results of these experiments In most cases considered here, deriving confidence intervals involves making a number of assumptions and approximations For example, the above confidence interval for errorv(h) involved approximating a Binomial distribution by a Normal distribution, approximating the variance of this distribution, and assuming instances are generated by a fixed, unchanging probability distribution While intervals based on such approximations are only approximate confidence intervals, they nevertheless provide useful guidance for designing and interpreting experimental results in machine learning.

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Tables can be customized Sometimes you might wish to format data differently than provided by the default UITableViewCell The most direct way to accomplish formatting a table cell is by adding a view to the UITableViewCell You do this in code, by adding a subview to a cell when initializing it in the tableView:cellForRowAtIndexPath method Another technique is to create a subclass of a UITableViewCell and then create its graphical layout using Interface Builder

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