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 want to create a best training sample from a given set of data points by way of running all possible combinations of train and test through a model and select based on the best R2.
I do not want to run the model with all possible combinations rather I want to select like a stratified set each time and run the model. Is there a way to do this in R.
sample dataset
df1 <- data.frame(
cbind(sno=1:30
,x1=c(14.3,14.8,14.8,15,15.1,15.1,15.4,15.4,16.1,14.3,14.8,14.8,15.2,15.1,15.1,15.4,15.4,16.1,14.2,14.8,14.7,15.1,15,15,15.3,15.3,15.9,15.1,15,15.3)
,y1=c(79.2,78.7,79,78.2,78.7,79.1,78.4,78.7,78.1,79.2,78.7,79,78.2,78.6,79.2,78.4,78.7,78.1,79.1,78.5,78.9,78,78.5,79,78.2,78.5,78,79.2,78.7,78.7)
,z1=c(219.8,221.6,232.5,213.1,231,247.6,230.2,240.9,245.5,122.8,124.2,131.5,119.1,130.5,141.1,130.8,137.7,140.8,25.4,30.5,30.5,23.8,29.6,34.6,29.5,33.3,35.2,105,170.7,117.3)
))
This defeats the purpose of training. Ideally, you have one or more training datasets and an untouched testing data set you'll ultimately test against once your model is fit. Cherry-picking a training dataset, using R-squared or any other metric for that matter, will introduce bias. Worse still, if your model parameters are wildly different depending on which training set you use, your model likely isn't very good and results against your testing dataset are likely to be spurious.
I did a deep learning model using keras. Model accuracy has 99% score.
$`loss`
[1] 0.03411416
$acc
[1] 0.9952607
When I do a prediction classes on my new data file using the model I have only 87% of classes well classified. My question is, why there is a difference between model accuracy and model prediction score?
Your 99% is on the Training Set, this is an indicator of own is performing your algorithm while training, you should never look at it as a reference.
You should always look at your Test Set, this is the real value that matters.
Fore more, your accuracies should always look like this (at least the style):
e.g. The training set accuracy always growing and the testing set following the same trend but below the training curve.
You will always never have the exact two same sets (training & testing/validating) so this is normal to have a difference.
The objective of the training set is to generalize your data and learn from them.
The objective of the testing set is to see if you generalized well.
If you're too far from your training set, either there a lot of difference between the two sets (mostly distribution, data types etc..), or if they are similar then your model overfits (which means your model is too close to your training data and if there is a little difference in your testing data, this will lead to wrong predictions).
The reason the model overfits is often that your model is too complicated and you must simplify it (e.g. reduce number of layers, reduce number of neurons.. etc)
I am attempting to solve a regression problem where the input feature set is of size ~54.
Using OLS linear regression with a single predictor 'X1', I am not able to explain the variation in Y - hence I am trying to find additional important features using Regression forest (i.e., Random forest regression). The selected 'X1' is later found to be the most important feature.
My dataset has ~14500 entries. I have separated it into training and test sets in the ratio 9:1.
I have the following questions:
when trying to find the important features, should I run the regression forest on the entire dataset, or only on the training data?
Once the important features are found, should the model be re-built using the top few features to see whether feature selection speeds up the computation at a small cost to predictive power?
For now, I have built the model using the training set and all the features, and I am using it for prediction on the test set. I am calculating the MSE and R-squared from the training set. I am getting high MSE and low R2 on the training data, and reverse on the test data (shown below). Is this unusual?
forest <- randomForest(fmla, dTraining, ntree=501, importance=T)
mean((dTraining$y - predict(forest, data=dTraining))^2)
0.9371891
rSquared(dTraining$y, dTraining$y - predict(forest, data=dTraining))
0.7431078
mean((dTest$y - predict(forest, newdata=dTest))^2)
0.009771256
rSquared(dTest$y, dTest$y - predict(forest, newdata=dTest))
0.9950448
Please suggest.
Any suggestion if R-squared and MSE are good metrics for this problem, or if I need to look at some other metrics to evaluate if the model is good?
You should also try Cross Validated here
when trying to find the important features, should I run the regression forest on the entire dataset, or only on the training data?
Only on the training data. You want to prevent overfitting, which is why you do a train-test split in the first place.
Once the important features are found, should the model be re-built using the top few features to see whether feature selection speeds up the computation at a small cost to predictive power?
Yes, but the purpose of feature selection is not necessarily to speed up computation. With infinite features, it is possible to fit any pattern of data (i.e., overfitting). With feature selection, you're hoping to prevent overfitting by using only a few 'robust' features.
For now, I have built the model using the training set and all the features, and I am using it for prediction on the test set. I am calculating the MSE and R-squared from the training set. I am getting high MSE and low R2 on the training data, and reverse on the test data (shown below). Is this unusual?
Yes, it's unusual. You want low MSE and high R2 values for both your training and test data. (I would double check your calculations.) If you're getting high MSE and low R2 with your training data, it means your training was poor, which is very surprising. Also, I haven't used rSquared but maybe you want rSquared(dTest$y, predict(forest, newdata=dTest))?
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.