I have a really large dataset and i'm trying to build a classification model using R.
However I need to use a train, test and validation set. But i'm a bit confused about the way to perform this. For example, I built a tree using a train set and then i computed the predicion using a test set. But I believe that i should be using the train and the test set to best tune the tree and after that use the validation set to validate. How can i do this?
library(rpart)
part.installed <- rpart(TARGET ~ RS_DESC+SAP_STATUS +
ACTIVATION_STATUS+ROTUL_STATUS+SIM_STATUS+RATE_PLAN_SEGMENT_NORM,
trainSet, method="class")
part.predictions <- predict(part.installed, testSet, type="class")
(P.S the tree is only an example. It could be another classification algorithm)
Usually the terminology is as follows:
The training set is used to build the classifier
The validation set is used to tune the algorithm hyperparameters repeatedly. So there will be some overfitting here, but that is why there is another stage:
The test set must not be touched until the classifier is final to prevent overfitting. It serves to estimate the true accuracy, if you would put the model into production.
Related
I'm using the R-package randomForest version 4.6-14. The function randomForest takes a parameter localImp and if that parameter is set to true the function computes local explanations for the predictions. However, these explanations are for the provided training set. I want to fit a random forest model on a training set and use that model to compute local explanations for a separate test set. As far as I can tell the predict.randomForest function in the same package provides no such functionality. Any ideas?
Can you explain more about what it means to have some local explanation on a test set?
According to this answer along with the package document, the variable importance (or, the casewise importance implied by localImp) evaluates how the variable may affect the prediction accuracy. On the other hand, for the test set where there is no label to assess the prediction accuracy, the variable importance should be unavailable.
I'm trying to build a predictive model, using the neuralnet package. First I'm spliting my dataset in training (80%) and test (20%). But ANN is such a powerful technique that my model easily overfits the training set and performs poorly on the external test set.
Predicted vs True Value - Training is the right one and test set is the left one
Is there a way to do a cross-validation on the training set so that my model doesn't overfit the set? How may I do this with my own built in function?
Plus, are there any other approaches when dealing with deep learning? I've heard you can tweak the weights of the model in order to improve its quality on external data.
Thanks in advance!
I have a problem to understand some basics, so I'm stuck with a regression tree.
I use a classification tree by rpart to check the influence of environmental parameters on a tree growth factor I measured.
Long story short:
What is the purpose of splitting data into training and test data and (when) do I need it? My searches showed examples in which they either don't do it or do it, but I can't find the backstory. Is it just to verify the pruning?
Thank you ahead!
You need to split into training and test data before training the model. The training data helps the model learn, while the test data helps validate the model.
The split is done before running the model, and the model must be retrained when there is some fine tuning or change.
As you might know, the general process for postpruning is the following:
1) Split data into training & test (validation) sets
2) Build decision tree from training set
3) For every non-leaf node N, prune the subtree rooted by N and
replace with the majority class. Then test accuracy with a
validation set. This validation set could be the one defined before
or not.
This all means that you are probably on the right track and that yes, the whole dataset has probably been used to test the accuracy of the pruning.
I am very new to machine learning. I have a question about running predict on data used for training set.
Here are details: I took a portion of my initial dataset and split that portion into 80% (train) and 20% (test). I trained the model on 80% of training set
model <- train(name ~ ., data = train.df, method = ...)
and then run the model on 20% test data:
predict(model, newdata = test.df, type = "prob")
Now I want to predict using my trained model on initial dataset which also includes the training portion. Do I need to exclude that portion that was used for the training?
When you report accuracy to a third person about how good your machine learning model works, you always report the accuracy you get on the data set that was not used in training (and validation).
You can report your accuracy numbers for the over all data set but always include the remark that this data set also includes the data partition that was used for training the machine learning algorithm.
This care is taken to make sure your algorithm has not overfitted on your training set: https://en.wikipedia.org/wiki/Overfitting
Julie, I saw your comment below your original post. I would suggest you edit the original post and include your data split to be more complete in your question. It would also help to know what method of regression/classification you're using.
I'm assuming you're trying to assess the accuracy of your model with the 90% of data you left out. Depending on the number of samples you used in your training set you may or may not have the accuracy you'd like. Accuracy will also depend on your approach to the method of regression/classification you used.
To answer your question directly: you don't need to exclude anything from your dataset - the model doesn't change when you call predict().
All you're doing when you call predict is filling in the x-variables in your model with whatever data you supply. Your model was fitted to your training set, so if you supply training set data again it will still create predictions. Note though, for proving accuracy your results will be skewed if you include the set of data that you fit the model to since that's what it learned from to create predictions in the first place - kind of like watching a game, and then watching the same game again and being asked to make predictions about it.
Let me start by saying that I have read many posts on Cross Validation and it seems there is much confusion out there. My understanding of that it is simply this:
Perform k-fold Cross Validation i.e. 10 folds to understand the average error across the 10 folds.
If acceptable then train the model on the complete data set.
I am attempting to build a decision tree using rpart in R and taking advantage of the caret package. Below is the code I am using.
# load libraries
library(caret)
library(rpart)
# define training control
train_control<- trainControl(method="cv", number=10)
# train the model
model<- train(resp~., data=mydat, trControl=train_control, method="rpart")
# make predictions
predictions<- predict(model,mydat)
# append predictions
mydat<- cbind(mydat,predictions)
# summarize results
confusionMatrix<- confusionMatrix(mydat$predictions,mydat$resp)
I have one question regarding the caret train application. I have read A Short Introduction to the caret Package train section which states during the resampling process the "optimal parameter set" is determined.
In my example have I coded it up correctly? Do I need to define the rpart parameters within my code or is my code sufficient?
when you perform k-fold cross validation you are already making a prediction for each sample, just over 10 different models (presuming k = 10).
There is no need make a prediction on the complete data, as you already have their predictions from the k different models.
What you can do is the following:
train_control<- trainControl(method="cv", number=10, savePredictions = TRUE)
Then
model<- train(resp~., data=mydat, trControl=train_control, method="rpart")
if you want to see the observed and predictions in a nice format you simply type:
model$pred
Also for the second part of your question, caret should handle all the parameter stuff. You can manually try tune parameters if you desire.
An important thing to be noted here is not confuse model selection and model error estimation.
You can use cross-validation to estimate the model hyper-parameters (regularization parameter for example).
Usually that is done with 10-fold cross validation, because it is good choice for the bias-variance trade-off (2-fold could cause models with high bias, leave one out cv can cause models with high variance/over-fitting).
After that, if you don't have an independent test set you could estimate an empirical distribution of some performance metric using cross validation: once you found out the best hyper-parameters you could use them in order to estimate de cv error.
Note that in this step the hyperparameters are fixed but maybe the model parameters are different accross the cross validation models.
In the first page of the short introduction document for caret package, it is mentioned that the optimal model is chosen across the parameters.
As a starting point, one must understand that cross-validation is a procedure for selecting best modeling approach rather than the model itself CV - Final model selection. Caret provides grid search option using tuneGrid where you can provide a list of parameter values to test. The final model will have the optimized parameter after training is done.