Questions tagged [train]

training (or estimation) of statistical models or algorithms.

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1answer
51 views

Why my validation accuracy and AUC are higher than my training accuracy and AUC?

I have a binary classification problem and I use LightGBM classifier to build my model based on 5 features. I divided my dataset (94 observations) into two parts: Training dataset: 60 observations ...
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3answers
302 views

How to quantitatively determine when to stop training ANN

I've implemented an artificial recurrent neural network and want to start training it on a variety of tasks. I've extensive searching online and haven't found a satisfactory answer of how the ...
1
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1answer
10 views

Can we apply SMOTE on data with k-fold CV

The SMOTE for the imbalance should be applied for the training data only, right? Can we still do it (perform SMOTE on training data) while we select the k-fold CV and does not go for splitting the ...
0
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1answer
172 views

Timeframe for training data in churn models vs prediction data - confused

I am developing a churn model for a subscription business. The churn rate is 7% yearly for it. The training data was prepared in such a way that customer information is tracked at the start of the ...
0
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1answer
14 views

Removing parameters from the training set helped training

I have a large dataset with 10s of millions of points in a 10 dimensional parameter space. I have tried training my regression neural network on the entire parameter space and got decent (ish) results....
0
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1answer
263 views

GBM Performance on different sampling techniques

I am working on a healthcare data set for breast cancer patients. This data set is class imbalances and the distribution of positive and negative classes is 80%/20%. In order to deal with the class ...
17
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3answers
8k 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 ...
1
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1answer
439 views

Extending the idea of Bootstrapping to Train Test splits of a Dataset used to learn a Classifier in Machine Learning

In Machine Learning the standard practice for learning a Classifier --e.g. fitting a Logistic Regression model-- and then validating its performance is to split the original/available Dataset into a ...
1
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1answer
214 views

Bootstrapping accuracy, f1-score?

I have typical train/test setting, with an ordinary dataset. As I am comparing performance of two approaches to a problem (namely churn prediction with AdaBoost and ...
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0answers
45 views

Specificity decreasing when new features are added to glmnet model for case/control prediction

I'm using glmnet for prediction of case/control, which I created with the function train with additional parameters for cross ...
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0answers
9 views

Cross validation for timeseries interpolation

I am trying to calculate the MSE from interpolating few data points obtained from a sensor. I am considering 100 datapoints and applying 10 fold cross validation to it. For explaining what i am doing ...
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0answers
17 views

Training a deep neural network using a changing (increasing) dataset

I am trying to train a deep autoencoder (applies also for other architectures) in the following way: Step 1) I start with a fix dataset of e.g. 10k samples. Step 2) A training "loop" consists of ...
0
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1answer
20 views

Randomize dataset for Restricted Boltmann Machines

Suppose I want to train a RBM (or even a DBN architecture) and then fine-tune the parameter training a Feedforward NN. In my case the dataset is composed of time series, so in principle there is a ...
1
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0answers
20 views

how to select training and testing data for interpolation in 100 instances data?

I would like to divide my data of only 100 instances into training and testing an use the training data to fit a curve(interpolate) and use the testing data to calculate the error at the interpolated ...
1
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1answer
53 views

Logistic regression - what is being predicted? [closed]

R has more than one way to create logistic regressions to predict binary outcomes. Here's the code that I'm using that is giving strange answers. ...
0
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0answers
14 views

synthesized data to train classifier

Our dataset is relatively small (303 x 14) and so we decided to use synthpop package in R. The basic idea of synthetic data is to replace some or all of the observed values by sampling from ...
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 ...
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0answers
12 views

Difference between retraining on different portions of data and training initially on larger data set

I have a large data set that doesn't fit in memory and would have to use something like Keras's model.fit_generator if I would like to train the model on all of the ...
6
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3answers
5k views

Is overfitted model with higher AUC on test sample better than not overfitted one

i am participating in a challange in which I have created a model that performs 70% AUC on train set and 70% AUC on hold-out test set. The other participant has created a model that performs 96% AUC ...
1
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1answer
23 views

Determining suitable seed number in R to randomly split data into training and test sets

Good day. I have sample size of 160 and I am randomly splitting them into 70% train and 30% test data sets. My question is about the set.seed() value which returns different random samples in each ...
2
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0answers
946 views

Random Forest online/incremental learning in R

Is there a Random Forest implementation available in R, that supports online learning? My alternative approach was to use the popular randomForest package and combine Random Forests (the existing one ...
1
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2answers
993 views

Interpreting Validation and Training loss

I have a quite big dataset of 10000 training data, i held out 2000 points for validation. I am using a Convolutional Neural Network and using minibatch stochastic gradient descent to minimize the RMS ...
1
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0answers
15 views

How do I check my GAN implementation is correct?

I wrote a GAN implementation and I trained that to produce some sample images after training on a dataset. The images looked visually fine. Now I want to test my implementation on the CI and make ...
2
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1answer
52 views

Data leakage when using walk forward optimization

I am setting up a neural network that will predict the incoming customers at a store for the next seven days (the output is a list with seven numbers, one for each day). As input, I will give the ...
0
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0answers
210 views

Very High Training Accuracy and very low Testing Accuracy CNN

I'm using 3 layer CNN with 8, 16, and 32 filters, each of size 5 X 5. I'm getting an training accuracy of 99.97%. Testing accuracy of 41.11%. Total classes: 605 Train Set: Each class has 7 samples ...
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0answers
13 views

oversampling data with subclass

Oversampling of under-represented data is a way to combat class imbalance. For example, if we have a training data set with 100 data points of class A and 1000 data points of class B, we can over ...
201
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5answers
144k views

Tradeoff batch size vs. number of iterations to train a neural network

When training a neural network, what difference does it make to set: batch size to $a$ and number of iterations to $b$ vs. batch size to $c$ and number of iterations to $d$ where $ ab = cd $? To ...
6
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2answers
6k views

How to train convolutional neural networks with multi-channel images?

I have $m$ labeled images, each with 224x224 pixels and 5 different image channels. What is the best way to train a CNN architecture using this data when $m$ is small (less than 2000)? Is it possible ...
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0answers
21 views

Regularized linear regression with class imbalance

I am trying to build a Linear Regression model using a not so big dataset. I'm more comfortable doing classification and I am not really an expert in regression. In classification, I was used to ...
1
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2answers
414 views

Is there a formula for a recommended batch size depending on the size of the training dataset?

I'm still training my neural network for gender/age classification, and I'm currently experimenting with batch sizes along with everything else. As I've gathered, too small a batch size will lower ...
4
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2answers
104 views

Practical realities of updating a trained model with new data

In my day to day work, I train models on data using R packages that have no extension for Bayesian priors. I will generally have a large dataset to start off with, and add new data as needed. Any ...
0
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1answer
30 views

What to do with very little training data

What are some popular solutions in dealing with very little training data? Do these solutions rely on generating more data (e.g. bootstrapping, SMOTE, etc.)? Or do they rely on methods that do not ...
1
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2answers
217 views

How to design a train and test set from a labeled dataset with class imbalance?

The labeled dataset I am using is almost 80% positive examples, 20% negative examples. However, I do not know the distribution of the data fed into the classifier. In this case, does it make sense ...
3
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1answer
76 views

Does retraining a model on all available data necessarily yield a better model?

A (simplified) typical workflow in machine learning might be: Train $m$ models on a training set. Validate the $m$ models on a validation set to yield the best model with parameters $\theta$. Retrain ...
1
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1answer
239 views

train / validation / test split

I understand that you typically use three different data sets (train/validation/test) to acquire an unbiased estimate of the performance measurement, because the models are tuned to fit for the train ...
0
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0answers
18 views

Splitting data into test and training when there are a low number of observations for a level within a variable

Context If a level within a categorical variable has less observations than there are elements in the training set then there's a chance that all of the elements of that level will be contained ...
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0answers
12 views

Different values in test / training data variable

The code and error in the following MWE represent my issue. code ...
0
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0answers
48 views

Credit Scoring WoE Calculation

I'm creating credit scoring model and stuck with WoE calculation. I know the formula and I know how to compute WoE for train sample. Should I use train sample WoE for test sample or I should compute ...
0
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1answer
40 views

What's this class of algorithms called: (entire training dataset, new input) -> output?

Supervised machine learning algorithms normally work by preprocessing a training dataset and outputting a compact model (e.g. a bunch of regression coefficients) that can quickly give an approximate ...
2
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1answer
42 views

Save Machine Learning Model progress for later [closed]

another dumb question, but how do you save the progress an ML model has made and start from that point later? Its kind of a vague question, but this is an example of what I am talking about: Say, ...
0
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0answers
16 views

ANN for Boundary Value Problem

I have a question regarding solving Boundary Value Problems (BVP) using ANNs. My understanding is that this is currently a challenging task. Most scientific literature on the subject is interested in ...
0
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1answer
17 views

With limited training time - should I shrink the training set and run more epochs?

I wonder what is considered a better practice in deep learning. I have a dataset of 100K images that I want to use for training a regional-CNN for a whole week. Is it possible that my network will be ...
2
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1answer
44 views

deep learning : how to retrain an already trained model on different data?

I have a model (a convolutional neural network) that has already been trained on set of training data for human pose estimation. It works very well for simple cases, but I want to make it more robust ...
2
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1answer
349 views

Amount of training data for classification accuracy

Is there an intuition or any relevant reading about the relationship between dimensionality of data, number of samples, model complexity and test accuracy of classification? E.g. for the simple cat/...
3
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0answers
218 views

How to train CNN for noise removal from images using Matlab [closed]

I am currently working on a project of mine where I want to use Convolutional neural networks for noise removal from images. I am talking about removing Poisson type of noise. The software that I am ...
1
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1answer
27 views

The performance metric used in prediction is different from the objective function to train the model

For linear regression and many machine learning models, we use the same performance metric during the training and testing stage. For example, during the training stage, our machine learning algorithm ...
3
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3answers
5k views

TfidfVectorizer: should it be used on train only or train+test

When training a model it is possible to train the Tfidf on the corpus of only the training set or also on the test set. It seems not to make sense to include the test corpus when training the model, ...
0
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0answers
122 views

What is the training algorithm for sklearn's Bayesian Ridge Regression?

I read the sklearn's code to train Bayesian Ridge Regression, but can not understand the algorithm. I think it is EM, but don't know where the update equations for lambda_ and alpha_ come from. Any ...
0
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0answers
23 views

Does maximizing model generativeness maximize its discriminative-ness?

If I train a model M in a way that maximizes the penalized likelihood of M given some data, is this equivalent to maximizing its ...
0
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0answers
341 views

RFECV + GRIDSEARCHCV on entire dataset (?)

I have 30,000 samples with 150 features for a binary classification problem, now I plan to follow: https://stackoverflow.com/questions/23815938/recursive-feature-elimination-and-grid-search-using-...