Out Of Sample Error
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Out Of Sample Error Random Forest
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Out Of Sample Performance
answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask a question https://rpubs.com/mtaufeeq/PML_Project Anybody can answer The best answers are voted up and rise to the top Difference between “in-sample” and “pseudo out-of-sample” forecasts up vote 2 down vote favorite 1 Probably a very basic question to the forecasters around here, but I was wondering whether there is an explicit difference between in-sample forecasts and pseudo out-of-sample forecasts. Both is meant in the context of evaluating and comparing forecasting models. forecasting http://stats.stackexchange.com/questions/74865/difference-between-in-sample-and-pseudo-out-of-sample-forecasts share|improve this question asked Nov 7 '13 at 13:11 altabq 3011413 add a comment| 1 Answer 1 active oldest votes up vote 7 down vote accepted Suppose you have data $\{Y_t,X_{t-h}\}_{t=h+1}^T$, where $h \in \{1,2,\ldots\},$ and your goal is to build a model (say, $\hat f(X_{t-h})$) to predict $Y_t$ given $X_{t-h}$. For concreteness, suppose the data is daily and $T$ corresponds to today. In-sample analysis means to estimate the model using all available data up to and including $T$, and then compare the model's fitted values to the actual realizations. However, this procedure is known to draw an overly optimistic picture of the model's forecasting ability, since common fitting algorithms (e.g. using squared error or likelihood criteria) tend to take pains to avoid large prediction errors, and are thus susceptible to overfitting - mistaking noise for signal in the data. A true out-of-sample analysis would be to estimate the model based on data up to and including today, construct a forecast of tomorrow's value $Y_{T+1}$, wait until tomorrow, record the forecast error $e_{T+1} \equiv Y_{T+1} - \hat f(X_{T+1-h}),$ re-estimate the model, make a new forecast of $Y_{T+2}$, and so forth. At the end of this exercise, one would have a sample of foreca
Tour Start 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 http://stats.stackexchange.com/questions/169754/out-of-sample-and-in-sample-testing Stack Overflow the company Business Learn more about hiring developers or posting ads with http://rstudio-pubs-static.s3.amazonaws.com/21171_2b53069217e84b6d9237608a1afff0a8.html us Cross Validated Questions Tags Users Badges Unanswered Ask Question _ Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers out of are voted up and rise to the top Out of Sample and In Sample testing up vote 0 down vote favorite 1 I am very confused in testing regressions and know that there are many explanations available online, but I am still not getting anything it in my mind. Suppose I have daily data for past 100 days, I run a simple linear regression estimate the parameters. Now there are three out of sample things: 1) If I just use the first 75 days of data and rerun the regression, I'll get slightly different parameters and then I can forecast the 76th day value of dependent variable through it and check the error comparing it with the original 76th day dependent variable value. Is this out of sample testing? 2) If the above is out of sample then what is in-sample testing? To be specific what would I use in regression and what to estimate? 3) If I use the original regression and estimate the 101th day dependent value then would it be a forecasting or another form of out of sample testing? Also for every next forecast, how would I know if I need to rerun the model adding more recent data or should I continue with the parameters derived from the first regression. regression forecasting out-of-sample in-sample share|improve this question edited Sep 2 '15 at 3:56 Dawny33 1,86111028 asked Sep 2 '15 at 3:30 Meesha 1034 add a comment| 1 Answer 1 active oldest votes up vote 2 down vote accepted Yes In-sample testing is looking at the errors of the first 75 days. Obviously the regression is already fitted to that data. If those errors are similar to the out of sample errors,
training a prediction model on the accelerometer data. The algorithm that I will be using for this exercise will be a random forest classifier. The first step is to load in the training data and subset it into a training and a testing set. I am sub-setting the training data to create an additional test set because I want to have a separate testing set that will give an unbiased estimate of the prediction model before the model has to classify on the actual test set. The Caret package will be used for data subsetting, training and cross-validation of the model library(caret) ## Warning: package 'caret' was built under R version 2.15.3 ## Loading required package: lattice ## Loading required package: ggplot2 ## Warning: package 'ggplot2' was built under R version 2.15.3 Training data subsets: # Global training data training <- read.table("P:/Coursera/pml-training.csv", header = TRUE, sep = ",") # remove the first 8 columns as those are just 'house keeping' columns for # the data training2 <- training[, -seq(from = 1, to = 8, by = 1)] # seed random # gen for subsetting set.seed(1234) # test subset: 30% of global training data inTest <- createDataPartition(y = training2$classe, p = 0.3, list = F) testSub <- training2[inTest, ] # training subset: 70% of global training data trainingSub <- training2[-inTest, ] The training data consists of 152 variables (160 ex first 8), but many of the variables are sparse, meaning that they only have observations for a few of the data points. These sparse variables may have predictive value, but because they are observed so infrequently they become fairly useless for classifying most of the data points that do not contain these observations. Therefore it makes sense to filter these inputs out and focus the prediction efforts on variables that have at least 90% of their observations filled in. # function for determining sparseness of variables sparseness <- function(a) { n <- length(a) na.count <- sum(is.na(a)) return((n - na.count)/n) } # sparness of input variables based on training subset variable.sparseness <- apply(trainingSub, 2, sparseness) # trim down the subs by removing sparse variables trimTrainSub <- trainingSub[, variable.sparseness > 0.9] Choosing my prediction algorithm: The predictor I intend to use for this classification problem is a random forest. The reasons I am employing a