1
vote
1answer
60 views

How do you validate your machine learning models?

I am wondering what approaches are commonly used for validating a classification or prediction models: Approaches that am using at the moment: Using truth-sets: - ROCs, Bootstrapping, Accuracy, ...
0
votes
1answer
21 views

Computing predicition intervals with cross-validation?

I'm using a k-fold (10-fold) cross-validation while building a model. I'm only using it to get an estimate of the out-of-sample error, not to pick a model from candidates. For example, if I have 30 ...
2
votes
2answers
55 views

Split train//validation/test sets by time, is it correct?

Here's the scenario, slightly altered to a common one. Credit card fraud, payments for the last 12 months (a rolling window). Train with the data from the first 10 months, validate with data from the ...
0
votes
0answers
21 views

Incorporating validation data into training set

Suppose that I divide my data for modelling purposes into training, testing, and validation which will then be deployed for an application (as in the response to this question). Why not incorporate ...
0
votes
0answers
12 views

How to validate classifier (built by using MLN method)?

I have developed a method (let's call it Method X) that has a classifier function. The classifier function was built by using MLN (Markov logic network). I need to ...
0
votes
1answer
27 views

highly sporadic validation error during training with multilayer perceptron

I'm encountering an issue where a classifier I'm developing reports validation errors during training that span a wide range of values without consistently decreasing over time. Unfortunately, I'm new ...
1
vote
2answers
68 views

Model instability in data mining. When it is big enough to discredit a model and how to measure it?

Let's say I have two models. One has cumulative lift on test data 4.322578, second 2.84488. The only advantage of the second over the first consists in the quality of having the cumulative lift curve ...
3
votes
2answers
222 views

k folds cross validation on a multi-class dataset

Cross validation is one of the most important tools because it gives us an honest assessment of the true accuracy of our system. In other words, the cross-validation process provides a much more ...
1
vote
0answers
488 views

Validation error less than training error — implications?

I am running a neural net to predict used car prices, sample size is 800. Using both 10-fold cross validation (10 times) and 1/3 holdback (10 times), the $R^2$ for training is about 0.60 and for ...
2
votes
1answer
86 views

Is a different CV arrangement the same as a validation set?

I have a smallish dataset ~ 1500 rows X 500 columns. I've been using a standard 5 fold CV setup where row 1 = CV set1, row2 = CV set2, ... row 6 = CV set1,etc. I'm at the point where I'm trying to ...
2
votes
2answers
692 views

What are acceptable validation or cross validation error rates?

Is there a commonly acceptable error rate for validation? As in, if the error rate is less than X %, then my machine learning method would be considered "successful". I'm looking for something ...
1
vote
1answer
642 views

How to select validation data when training a neural network?

I am training a neural network with time dependent financial data. In order to avoid overfitting I would like to stop the training at the point where my neural network stops improving on a set of ...
30
votes
5answers
22k views

What is the difference between test set and validation set?

I found this confusing when I use the neural network toolbox in Matlab. It divided the raw data set into three parts: training set validation set test set I notice in many training or learning ...
4
votes
1answer
1k views

Best practices for measuring and avoiding overfitting?

I am developing automated trading systems for the stock market. The big challenge has been overfitting. Can your recommend some resources describing methods for measuring and avoiding overfitting? I ...