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Questions tagged [train]

training (or estimation) of statistical models or algorithms.

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14 views

Predicting multiclass outcome using R

The dataset contains about 17000 observations, the outcome variable (with about 10 classes) and the predictors (about 10) are all categorical variables. Which algorithm is the best to employ to train ...
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1answer
22 views

About the need of splitting data in stacking

I learned stacking of machine learning in a book, hands-on machine learning 2nd edition (2019). The picture was cited from hands-on machine learning 2nd edition (2019). In the above situation, ...
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14 views

Aggregate data for machine learning. Weights or fake disaggregation?

I have a dataset of medical centers and I need to predict their infection rate, based on the center characteristics and aggregated patient data (eg. percentage of patients which underwent a certain ...
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23 views

Gaussian process with ARD kernel much more expensive to train

I'm fitting a Gaussian process regression model in MATLAB (using the quasi-Newton method) with 10 input parameters, using the Matérn 5/2 and Matérn 5/2 ARD kernels. I notice that, with increasing ...
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35 views

Why backpropagation if loss function is not convex in nature?

Backpropagation contains the method of gradient decent, which works well for convex loss functions with a global minima. But, for training, in most of the neural network tasks, backpropagation is ...
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1answer
20 views

Test score bigger than Train score in Linear Regression

I'm new to ML and I'm trying to create a linear regression model. My data consist of 100 samples with 4 features each. This is my humble code ...
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1answer
75 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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1answer
14 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 ...
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1answer
15 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....
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10 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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47 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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18 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 ...
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22 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 ...
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1answer
56 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. ...
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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 ...
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19 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 ...
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13 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 ...
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1answer
30 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 ...
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19 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
65 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 ...
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229 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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14 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 ...
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24 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 ...
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1answer
31 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 ...
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1answer
114 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 ...
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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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14 views

Different values in test / training data variable

The code and error in the following MWE represent my issue. code ...
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0answers
65 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 ...
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1answer
43 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
44 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, ...
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19 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 ...
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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 ...
4
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2answers
110 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 ...
3
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0answers
281 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 ...
2
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1answer
75 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 ...
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1answer
31 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 ...
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133 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 ...
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30 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 ...
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400 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-...
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37 views

If I remove 20% of the train examples, my CV and train score improves, how can I find the reason why?

I have a small dataset - several thousands examples with a lot of features, a lot of them have a lot of zeros. If I remove 20% of examples from the end of the dataset - my CV and train(on changed ...
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0answers
13 views

Why after multiple epoc the performance decrease on the train data?

I have a network and I notice that after multiple epoc the performance on the train data start decreasing. Why?
2
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1answer
123 views

How to meaningfully compute the accuracy of a multi-step forecast produced by a model

I am trying to measure the accuracy of my model in producing a multi-step forecast and I have read a lot of different opinions on the matter and am now rather confused. The goal of my model is to ...
2
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1answer
381 views

in-sample data vs out-of-sample data

I know that a train-validation-test splits the data into: a training dataset - obviously my "in-sample" data a validation dataset a test data set - obviously my "out-of-sample" data My question is: ...
3
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2answers
423 views

Can overfitting be a good thing in some cases?

I know the goal of machine learning is to create generalizable models and therefore overfitting is undesirable. However, I wonder if it could be desirable in some cases. For example, let's say I ...
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25 views

How to test a PCA+classifier model? [duplicate]

I have a 100x45 dataset and I'd like to perform feature selection and classification/regression. I'm currently using various techniques to check which one has the best performances, but I have a ...
0
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1answer
178 views

Why is gradient descent and it's variants used instead of BFGS and L-BFGS for training neural nets? [duplicate]

My understanding is that BFGS and L-BFGS solve the same type of optimization problems as GD and it's variants. Why is GD the go to algorithm for training neural networks?
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2answers
118 views

Data set splitting for statistical inference?

probably a very basic question -- I am modelling companies' decisions on which mode of payment they use in M&A deals with a help of logit model. I am so far interested only in what variables are ...
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0answers
482 views

Drawing ROC curves from RFE() training results in caret

I want to generate ROC curves using the training data and results from the rfe function in caret. I have managed to do this with the code below but there is some inconsistency between the ROC value ...
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1answer
15 views

Should the distribution of my samples in my model be equal to to the original data set?

In my original data set I see a distribution of 70% belongs to label A and 30% belongs to label B. For my train, validation and test set I maintained the same ratio. However, I wonder whether this is ...
2
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1answer
68 views

Standardization on training only or also including testing data?

My question is very much related to this one: How to apply standardization/normalization to train- and testset if prediction is the goal? However, my testing data is not a single observation that I ...