Questions tagged [train]

training (or estimation) of statistical models or algorithms.

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11 views

Oversampling/Undersampling in respect to Train and Test - Isolation Forest

I've got a quite imbalanced data set. 144.496 : 162 -> ratio of 1000:1 I would like to use IsolationForest to detect the 162 anomalys. I've already split the data. However, the iForest doesn't ...
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14 views

Validation loss is decreasing, accuracy is decreasing too

So, I have the following charts from my experience.Can any one explain why accuracy is decreasing while the loss in train and validation is decreasing? The point is that i can't early stop too in the ...
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0answers
10 views

object detection loss

I have trained an ssd detector in my own dataset and the values of train loss and val loss are shown in the picture. However in all the epochs the value of val loss is lower than that of train loss ? ...
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13 views

I have capped my response variable, should I calculate my RMSE/MAE/MAPE with the true values capped or not?

So, I have trained a model in my train set with the response variable with a superior limit. Because, the peaks are not important for my analysis. And if I dropped it, I would lost a lot of data. ...
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1answer
21 views

Should a model be trained until it is stable to find optimal hyperparameters?

A model may take several days to train until it reaches an equilibrium - say if the change in error between epochs is lower than some threshold $\epsilon$, or accuracy reaches some equilibrium. When ...
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6 views

Is it possible to achieve both stratified sampling and keeping the same train/test dataset split across different runs?

Generally, it is suggested to sample a dataset such that test set and train set remain the same when running the code multiple times, for comparison but also to hide your algorithm the whole dataset. ...
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1answer
18 views

should I re-initialize my optimizer and my scheduler before I try to fine tune my neural network on the different dataset?

I am doing NLP, and I have this block of Transformer body that was already trained on dataset A. Now I am interested in fine tuning this same Transformer on a new dataset B. In my Python code, should ...
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3answers
315 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 ...
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5answers
2k views

Can increasing the amount of training data make overfitting worse?

Suppose I train a neural network on dataset A and evaluate on dataset B (that has a different feature distribution than dataset A). If I increase the amount of data in dataset A by a factor of 10, is ...
20
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1answer
19k views

Benefits of stratified vs random sampling for generating training data in classification

I would like to know if there are any/some advantages of using stratified sampling instead of random sampling, when splitting the original dataset into training and testing set for classification. ...
2
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1answer
74 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 ...
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3answers
152 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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2answers
604 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 ...
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1answer
38 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, ...
2
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1answer
135 views

Why do we have to be concerned about the problem of overfitting on the training set?

For a hypothesis set $H=\{h_1,...,h_M\}$, randomly sampled training set $D_{train}$, and a learned hypothesis $g$ using $D_{train}$, the VC-bound of a finite hypothesis set tells us $$ P[|E_{in}(g)-...
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29 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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1answer
140 views

Can I use cross validation on a subset of the training set to select hyperparameters?

I am using R, and I had a dataset with 400000 rows and 800 columns, training a random forest model with only 100 trees on this dataset will take me about 1 and half hour on my laptop. So I went on and ...
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15 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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0answers
47 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
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 ...
4
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1answer
599 views

When to stop training of neural network when validation loss is still decreasing but gap with training loss is increasing?

During training of CNNs, I often come across this case for training and validation loss : X axis is epochs, Y axis is cross entropy loss. I would like to keep the "best model", meaning the one which ...
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2answers
1k 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 ...
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1answer
23 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
145 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 ...
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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 ...
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1answer
115 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
17 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
185 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 ...
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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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1answer
273 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 ...
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3answers
10k 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 ...
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1answer
294 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
48 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
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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0answers
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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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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0answers
27 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
57 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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0answers
23 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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0answers
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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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
39 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
965 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 ...
2
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0answers
22 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 ...
3
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1answer
83 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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248 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
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 ...
232
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5answers
167k 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 ...
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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
25 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 ...