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Resubstitution Error Decision Tree

here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this site About Us Learn more about Stack Overflow the company Business Learn classification error rate decision tree more about hiring developers or posting ads with us Stack Overflow Questions Jobs Documentation Tags what is root node error Users Badges Ask Question x Dismiss Join the Stack Overflow Community Stack Overflow is a community of 6.3 million programmers, just like you, how to calculate accuracy of a decision tree helping each other. Join them; it only takes a minute: Sign up How to compute error rate from a decision tree? up vote 20 down vote favorite 13 Does anyone know how to calculate the error rate

Misclassification Rate Decision Tree

for a decision tree with R? I am using the rpart() function. r classification decision-tree rpart share|improve this question edited Jan 29 '13 at 9:09 rcs 36.1k10120127 asked Mar 12 '12 at 11:29 teo6389 1431210 add a comment| 1 Answer 1 active oldest votes up vote 38 down vote accepted Assuming you mean computing error rate on the sample used to fit the model, you can use printcp(). For example, using the on-line example, > library(rpart) decision tree classifier > fit <- rpart(Kyphosis ~ Age + Number + Start, data=kyphosis) > printcp(fit) Classification tree: rpart(formula = Kyphosis ~ Age + Number + Start, data = kyphosis) Variables actually used in tree construction: [1] Age Start Root node error: 17/81 = 0.20988 n= 81 CP nsplit rel error xerror xstd 1 0.176471 0 1.00000 1.00000 0.21559 2 0.019608 1 0.82353 0.82353 0.20018 3 0.010000 4 0.76471 0.82353 0.20018 The Root node error is used to compute two measures of predictive performance, when considering values displayed in the rel error and xerror column, and depending on the complexity parameter (first column): 0.76471 x 0.20988 = 0.1604973 (16.0%) is the resubstitution error rate (i.e., error rate computed on the training sample) -- this is roughly class.pred <- table(predict(fit, type="class"), kyphosis$Kyphosis) 1-sum(diag(class.pred))/sum(class.pred) 0.82353 x 0.20988 = 0.1728425 (17.2%) is the cross-validated error rate (using 10-fold CV, see xval in rpart.control(); but see also xpred.rpart() and plotcp() which relies on this kind of measure). This measure is a more objective indicator of predictive accuracy. Note that it is more or less in agreement with classification accuracy from tree: > library(tree) > summary(tree(Kyphosis ~ Age + Number + Start, data=kyphosis)) Classification tree: tree(formula = Kyphosis ~ Age + Number + Start, data = kyphosis) Number of terminal nodes: 10 Residual mean deviance: 0.5809 = 41.24 / 71 Misclassification error rate: 0.1

different decision trees. Both are correct over the "training cases" --- the set of games whose outcomes we already know. And they have comparable size. Note, however, they give different answers here: decision tree pruning The current tree, based on Gain Ratio, claims that the MallRat's will win. Two

Classification Tree

different splitting criteria, leading to two different trees, leading to two different outcomes. Which outcome should we predict --- ie,

Decision Tree In R

which tree should we believe? To help address this... recall that our goal is to predict the unknown outcome of a future game --- a challenge which is subtly different from correctly predicting the http://stackoverflow.com/questions/9666212/how-to-compute-error-rate-from-a-decision-tree outcome of the known games. Here, it would be useful to see, for example, whether either tree could correctly predict the outcome of tomorrow's game, between the MallRats and the SnowBlowers. This is not our immediate task, which is to predict the outcome of next week's MallRat/Chinook game; we hope to use it, however, to help us determine which tree seems more correct. Of course, we need to https://webdocs.cs.ualberta.ca/~aixplore/learning/DecisionTrees/InterArticle/6-DecisionTree.html know the outcome of that MallRat/SnowBlower game, before we can use it in evaluating our two trees... which is not known, as that game has not been played. However, we can use this basic idea -- of evaluating our learners based on the performance of their trees on unseen examples. The challenge, of course, is finding a source of "unseen examples": examples that the learner has not seen, but which we can see, and then use to evaluate the performance of the various classifiers obtained. Why not use the examples we already have? For example, rather that train on all 20 games, we could instead train only on the first 19 games --- ie, not show the learner the final game: Game# Where When Fred Starts Joe offence Joe defense Opp C OutCome 20 Away 5pm No Center Center Tall Lost (The complete dataset is here.) The two learners (using Information Gain and Gain Ratio resp.) would each learn their respective trees based only on the first 19 games. We could then see which tree did best on the (unseen by the learner) 20th game. Now if we find the InfoGain-based tree was correct on this 20th game but the GainRatio-based tree

are expected to have an influence on the target variable, and are often called predictor variables. The prediction is done by constructing a https://help.alteryx.com/9.5/rpart.htm set of if-then split rules that optimize a criteria. The criteria used to form these rules depends on the nature of the target variable. If the target variable identifies membership in one of a set of categories, then a classification tree is constructed based on maximizing the "purity" at each split based on Gini coefficient or an entropy based information indext. If the target decision tree variable is a continuous variable, then a regression tree is constructed using the split criteria of minimize the sum of the squared errors at each split. The Wikipedia article on decision tree learning, and the references there in, provide additional information on the algorithms used.* With this tool, if the input data is from a regular Alteryx data stream, then the open source R rate decision tree rpart function is used for model estimation. If the input comes from either an XDF Output or XDF Input tool, then the Revo ScaleR rxDTree function is used for model estimation. The advantage of using the Revo ScaleR based function is that it allows much larger (out of memory) datasets to be analyzed, but at the cost of additional overhead to create an XDF file and uses an algorithm that needs to make more passes over the data (so is slower) than the open source rpart function. Note: This tool uses the R tool. Install R and the necessary packages here: http://downloads.alteryx.com/Latest_RInstaller.htm Input An Alteryx data stream or XDF metadata stream that includes a target field of interest along with one or more possible predictor fields. Configuration There are three tabs to configure for the Decision Tree tool: Required parameters, and optional Model Customization and Graphics Options. Required Parameters Model name: Each model needs to be given a name so it can later be identified. Model names must start with a letter and may contain letters, numbers, and the special characters period (".") and underscore ("_"). No other special c

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