Cross Validated

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training (or estimation) of statistical models or algorithms.

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1answer
4k views

Card Games and Neural Network Inputs

Looking for some pointers on how best to structure a neural net that deals with a card game. In Gin (rummy), you have a 10-card hand and you're trying to make melds and sets out of your cards. A meld ...
0
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1answer
144 views

Cross-Validation in binary classification using only 10 positive samples (SVM)

I have a binary classification problem for which only $10$ positive samples are available for training. Negatives are in general in abundance, but I choose to use solely $70$ ($7$ negatives per one ...
2
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2answers
3k views

How to divide dataset into training and test set in Recommender Systems?

I am working on two simple recommender systems - Collaborative (item-item a user-user) and Content Based. I would like to evaluate prediction accuracy of these systems. I am used to divide dataset ...
6
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1answer
162 views

How to train a model when instead of a target we have a range where it is?

Often in machine learning we have a situation when target is numeric (real or integer). Each target comes with an associated input vector. The goal is to learn the mapping from the input vectors to ...
14
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2answers
16k views

Different results from randomForest via caret and the basic randomForest package

I am a bit confused: How can the results of a trained Model via caret differ from the model in the original package? I read Whether preprocessing is needed before prediction using FinalModel of ...
1
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0answers
134 views

Is there any relationship between Train error and Test error in linear regression?

I am doing regularization on linear regression and am observing something arguable: By increasing the $\lambda$ (shrinkage value), I observe that there is a point in which the training and testing ...
5
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2answers
1k views

Should I use epochs > 1 when training data is unlimited?

If I have virtually endless training data (it's synthesized) is there still purpose in having epochs? I.e. training on the same samples multiple times?
0
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0answers
62 views

What is the best practices as to cropping the positive examples for training hog classifier?

I've been reading up on the literature on HOG training, but I can't figure out what the best practices are for cropping the positive examples. Question: Do you crop tight or with margins on the “...
3
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1answer
1k views

Test Error less than cross-validation error-implications?

If the test-set RMSE error of a model is less than cross-validated RMSE error, how can I interpret this? Is this abnormal? Does it imply a mistake in the methodology?
0
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1answer
909 views

training and testing an artificial neural network method

Is it alright to apply different epoch numbers to train/test a ANN method ( i.e. set number of iterations/epoch for training mode, and then set another different number of epoch for testing mode) ?
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0answers
294 views

Can you take a DNN that was trained without regularization, and continue training it with regularization?

If I've trained a DNN with out any regularization methods (e.g. weight decay, dropout etc.) and reached a good training error, can I somehow take that learned net and fine tune it with regularization? ...
4
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1answer
715 views

proportion between test and train set in regression and classification

Many claim different proportion between test and train set in regression and classification telling this should be test:train : 0.25:0.75 or 0:33:0:66 (the most popular with what I met). But what with ...
1
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1answer
2k views

How we can add new data in training time of neural network without stopping it in MATLAB?

I have a binary classification problem. Now I'm using patternnet in MATLAB R2014b to design a neural network for this problem. ...
1
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0answers
120 views

Adaptive Learning Rate Convolutional RBM?

I was wondering if anyone was aware of some work done for Adaptive learning rate for Convolutional RBM training? KyungHyun Cho published an algorithm for RBM (Enhanced Gradient and Adaptive Learning ...
1
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0answers
537 views

Training Neural Network with Highly Correlated Inputs [duplicate]

I am trying to train a basic Neural Network to predict Football final scores based on: i) Time in the match ii) Current Score iii) Parameters representing strength of home and away team. In order ...
0
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1answer
98 views

Once you have a set of training data, how do you go about training a model?

I've been trying to teach myself machine learning using some books, kaggle and other resources so I'm still very new at this. I'm trying to do a competition on kaggle and I've obtained a dataset. I'...
4
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0answers
856 views

Pre-training deep neural networks by supervised learning

When pre-training deep neural networks layer by layer, is it normal to pre-train the layers -which haven't been pre-trained by unsupervised training- by using supervised training before we train the ...
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0answers
118 views

semisupervised classification training on all or part of unlabeled data

I have 3 sets of data. A positively labeled dataset. An unlabeled dataset that has for sure positive (around 75%) and negative data. An unlabeled dataset that has for sure positive data and maybe ...
2
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0answers
103 views

Expected required sample length to train a hidden Markov model

Say one wishes to train a hidden Markov model with $n$ hidden states, and (accidentally) the problem itself can be described with a hidden Markov model with $n$ (or less states). What is the expected ...
4
votes
1answer
2k views

Tips for training dropout neural network

I use NN for my mini project research, and I found out the newest trick for feed forward NN is using dropout for regularization instead of L1/L2 norm and rectified linear unit as an activation ...
1
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1answer
336 views

How to compute the weights gradients for Convolutional RBM with CD?

I've successfully implemented a RBM and trained it with CD-k, but I now have some issues doing the same with Convolutional RBM. The visible and hidden biases gradients are easy to compute: v1 - v2 ...
0
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1answer
1k views

how to handle (many) false positives in training dataset for logistic regression classifier

I want to train a logistic regression dataset. I have a quite big training data set ( >100 000) and have around 10 features I can train on. Half of my training data is negative training data and I ...
3
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1answer
1k views

Latent semantic classification

How can I create a training data set for document classification using LSA? I have created a term-to-document matrix and have class labels also. I don't know whether to add these class labels in a ...
0
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1answer
2k views

How to speed up the training in neural network when mini-batch training is used?

Can anyone give me some ideas on possible techniques to speed up the training process of multilayer artificial neural network if the training involves mini-batch? So far, I understand that stochastic ...
3
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0answers
1k views

HMM library, different length sequences training

I'm using the Kevin Murphy's HMM library in MATLAB(http://www.cs.ubc.ca/~murphyk/Software/HMM/hmm.html) There is a section called 'How to use the toolbox'. There is this example for GMM ouputs: <...
2
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2answers
778 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 ...
2
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2answers
1k views

Building training set from unlabeled data

I want to build classifier using naive bayes. I know that naive bayes is supervised learning that requires labeled data for training set. However, I only have data with no label on it. Is there any ...
4
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3answers
2k views

Neural network packages which allow shared weights and parallel training

I'm curious if there are any neural network packages out there that easily allow one to construct feed forward neural networks with shared weights, but also allow for the training to be done in ...
18
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3answers
9k views

Imputation before or after splitting into train and test?

I have a data set with N ~ 5000 and about 1/2 missing on at least one important variable. The main analytic method will be Cox proportional hazards. I plan to use multiple imputation. I will also be ...
14
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3answers
4k views

Training, testing, validating in a survival analysis problem

I've been browsing various threads here, but I don't think my exact question is answered. I have a dataset of ~50,000 students and their time to dropout. I am going to be performing proportional ...
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0answers
1k views

k-means + linear regression: How to split the data for validation

I want to cluster my data first using k-means and then determine a regression model for each cluster. Then I want to evaluate the performance of this approach using split validation. I can think of ...
6
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1answer
7k views

How to perform parameters tuning for machine learning?

I have a very basic question regarding parameter tuning using grid search. Typically some machine learning methods have parameters that need to be tuned using grid search. For example, in the ...
1
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1answer
246 views

Improvement on duplicating instances

I have a task of Relationship extraction. There are some set of predefined relations in the corpus. I need to train classifier to recognize the type of relation or the lack of relation between every ...
2
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0answers
373 views

CV.glmnet results [duplicate]

I was working through the lab on ridge regression and LASSO in ISLR and I came across a strange behavior in the cv.glmnet function. When I followed the lab as ...
3
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1answer
4k views

What is the best strategy to train and validate classification using PLS-[classifier] in caret package?

I have 4 clusters (see plot below) extracted from data of medical samples N=218 measured for 11 genes/predictors P=11 by this ...
1
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1answer
121 views

Linear regression from data that don't represent a function

I have $(x,\ y)$ pairs with a strongly suspected linear correlation. So I want to fit the "best" linear function in order to make predictions for unknown $x$'s. These pairs don't represent a function, ...
3
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1answer
1k views

Data split into training and test

I am implementing an EEG classifier with 15 subjects (patients), specifically a support vector machine classifier. I randomly choose the training and testing sets, but I was faced by a question "how ...
0
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0answers
1k views

Metric warning using caret's rfe

I am using the caret package to do feature selection with rfe while training a knn ...
2
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2answers
411 views

Is it always bad to retrain your model to include predicted data?

I understand intuitively why this is a horrible idea - you assume your model is correct and then increase your number of observations which will likely result in a poor fit on future data. I'm ...
1
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2answers
71 views

Is “model selection” the same as traning?

A terminology problem. In machine learning we have the following problem: Choosing the optimal model (or training): $$ f^* = \arg\min_{f \in \mathcal{F}} \sum_i l(x_i,y_i) $$ Is the term "model ...
1
vote
1answer
4k views

R caret difference between ROC curve and accuracy for classification

In case of caret package test function metric option, one can use either accuracy or ROC as a metric that will be used to finalize values of tuning parameters. I felt that accuracy and ROC are the ...
3
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1answer
5k views

LGOCV caret package R

i am learning data mining through book . During classification chapters about Neural Networks the authors have below code. I have below questions: ...
4
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3answers
3k views

Text Annotation tools

I have tried open nlp NER for extracting organization names with not great success (It could be model is not fit for the domain I am working). So, I am planning to train Open NLP NER on my training ...
3
votes
1answer
656 views

Feature selection in the training set

I have a classifier, and I am using leave one out cross-validation to assess its performance. On each iteration, I divide the dataset into training and testing sets. The testing set is just the ...
3
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1answer
935 views

Methods for time-series prediction depending on multiple parameters

We have hourly time-series data of the status of a system: number of people present at different train stations. We collected it for a year, and we want to use it to train a model to predict the ...
1
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0answers
242 views

Forming training set for Multinomial Naive Bayes

Is it true that Multinomial Naive Bayes requires equally by count training data for each class to get best performance? For example, we forming classifier for three classes - Japan, China, Korea. ...
1
vote
2answers
477 views

Combining labeled and unlabeled data for training

When training my algorithm, if I can get some i.e. data my future test data that has no labels can it improve my algorithm's efficiency, is there any mathematical proof for it? PS: I think semi-...
5
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1answer
1k views

Logistic Regression Cost Function issue in Matlab

I'm trying to implement a logistic regression function in matlab. I calculated the theta values, linear regression cost function is converging and then I use those parameters in logistic regression ...
3
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1answer
811 views

SVM retrain on whole dataset for final model --> overfitting?

i am training a SVM (RBF kernel) with a dataset of ~1500 samples (balanced) using fminsearch on the CV error for parameter optimization (C and s). After i found the "best" parameters (local optima ...
0
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1answer
695 views

What is Generalization errror on training set. How can I see it on weka?

I get output like this .. ...

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