Questions tagged [train]
Training (or estimation) of statistical models or machine learning algorithms.
346
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about training disease detection model
We are doing a study on disease detection in the field of health. For a better result. Should the model be trained only with the unhealthy data set or should the healthy data set also be given to the ...
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Are this steps towards improve pretrained VGG16 results correct
Context
tldr you can skip to the bottom to see the question. I just add some basic information about the problem below.
I have been testing my VGG16 based network in this cases
Stop signs (small ...
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Mixed logit and predicted shares
In Train 2009, pg. 323, he writes:
... maximum likelihood estimation
of a standard logit model with alternative specific constants for
each product in each market necessarily gives predicted shares ...
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Support Vector Machine issue with caret [closed]
So I am training a linear support vector machine on my dataset using lssvm function from kernlab package. I get this error that ...
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Updating models with new data: how much is needed to keep a model accurate?
How do you update a model when the implementation of your model eliminates new data?
I have created a boosted trees classification model that predicts whether or not the amount of money requested (<...
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Linear regression : The value of R2 increasing with the incraese of the number of K folds when using cross validation : is it a good thing?
Let's say I have a dataframe with one dependent continuous variable and multiple independent categorical and continuous variables. I want to apply linear regression (using R language in my case). The ...
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1
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Capping before or after splitting the data into train and test?
I have a data set with N ~ 9000 and about 50% missing on at least one important variable. There are more than 50 continuous variables and for each variable, the values after 95th percentile seems ...
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Training Dataset preparation for predicting customer Churn at a specific Month
I have dataset of customers from 2019-2022 . My goal is to predict customer Churn at a specific point in time , say exactly 3 months from the observation point ...
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How to correctly train a model with a data generator
I'm working on a project where a Machine Learning Model (CNN) have to recognize handmade signs on paper cards starting from a camera picture.
It' a POC and I don't have the actual pictures, so have to ...
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Relationship between number of trainable parameters and training time
I have been using Keras Conv1D layer to train a forecast model. Like this:
...
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Should I run a Random Forest on split data with 1000 observations?
I have a dataset of 1000 observations. I'm performing a conditional inference tree/random forest on it soon.
I'd like to know if since it is so small if I should split the data into training and ...
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Ensuring memorization doesn't happen between between train and test sets in a Machine Learning model
Recently, contractors developed an NER solution for us which extracts relevant drugs out of pharmaceutical policies (drugs that the policy was describing coverage criteria for). Part of their process ...
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Why the validation error does not decrease with a training of 30 epochs but decreases with a training of 60 epochs
When i train my model with 30 epochs, the training and validation error curves seems to stagnate:
However, when i train my model with 60 epochs, the training and validation error curves start to ...
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Do I need to train a pretrained model?
Suppose I found on the Web (e.g. Github) a neural network model which perform object recognition.
The repository provides weights for this model and it provides also a train folder with tools/script ...
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The network's train loss and validation loss become low after only a few epochs
I am using wireless data(I-Q data) to do regression task, and I just use the amplitude of that data as input. The backbone of the network is 1 Conv-BN-RELU layer, 4 ResNet Blocks and a fully ...
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Trained EfficientNetB0 does not seem to make an impact on evaluation
I am using EfficientNetB0 to detect steganography in an image. It has been previously described in a research paper that under certain conditions it can work.
The parameters for the training are
$...
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Is it possible and what's meaning if model residuals were to have mean zero on training data but non-zero mean of residuals on test data?
Is it possible and what's meaning if model residuals with mean zero on training data but non-zero mean residuals on test data?
My guess is that the model produces biased estimates.
user318514
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279
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On using the same tokenizer for train and test data
I have used keras.preprocessing text tokenizer to fit on the training data alone, computed the (train) vocab size 'input_dim' and maximum train sequence length 'input_length' before fitting my neural ...
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What is the MLE for regression machine learing models? [duplicate]
From my understanding, in linear regression maximizing the log-likelihood function is equivalent to maximizing the negative MSE. But what about other common regression machine learning models ...
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add more data to training set
I am using the LinearSVC() available on scikit learn to classify texts into a max of 7 seven labels. So, it is a multilabel classification problem. I am training on a small amount of data and testing ...
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How to apply knowledge distillation using student-teacher model if we have different input sizes for student and teacher networks
I already trained student-teacher networks using the main idea of knowledge distillation which has a form of (source of image)
I wondered if there is a way to use a different input size (already used ...
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Does a distribution of predictions similar in the test dataset and in an unlabellised dataset means that the model will generalize well?
I have trained a logistic regression model on a train dataset. The prediction on the test set is very good, with a nice distribution of the predictions : a high peak around 0 for the true 0s and a ...
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Can a neural network fail to replicate the training data if trained on a very small dataset?
I created a neural network and I have trouble getting it to train. I followed all advice in this post: What should I do when my neural network doesn't learn?
but have not had success yet.
Then I ...
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Why does a neural network have the same output for every item in a batch?
I am trying to train a small MLP in Pytorch.
Here is the code for the net:
...
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How to compute score on separate test set after k-fold cross validation on separate train set?
I am aware there is quite a few similar questions but none answer was dealing with following situation:
I have a task with train dataset and test dataset provided. All previous approaches are measured ...
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Optimal number of steps per epoch for maximum accuracy on neural networks
This question has a very good answer discussing optimal mini-batch size for training neural networks, that points out that the final accuracy of a model usually decreases when using very large batches ...
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Calculating training loss over epochs
I have to calculate loss over different epochs 10,20,30 & 40.
In this case should I run the algorithm ONCE till 40 epochs and note the loss at 10,20,30 and 40 epochs? Or should I re-run the ...
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1
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Dealing with very small and unbalanced data
I am working on some TV series data, so the number of records is very limited. I have 58 instances, one for each existing episode, which I have randomly split in 45 and 13. The main goal is to make a ...
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State-of-the-art techniques for regularizing Neural Networks?
For regularizing neural networks, I'm familiar with drop-out and l2/l1 regularization, which were the biggest players in the late 2010's.
Have any significant/strong competitors risen up since then?
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Number of epochs and weight updates in deep models
Does training any model from scratch require more or less updates compared to fine-tuning a pre-trained model?
For cancer disease classification, I have built a network from scratch, with batch size ...
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2
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231
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Comparing impact of training data size - what testing data size?
I am training a classifier using BERT and want to check how the accuracy changes with increasing training data size. Up until now, I have 1k annotated training samples and tested the accuracy for ...
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Are there any way of removing impact of a certain data from a trained model (about "right to forget")
I was reading about wearable technologies (Recent Advances in Wearable Sensing Technologies). They briefly talk about Right to forget and a question came to my mind. Suppose that we trained a deep ...
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Does it make sense that the loss function for traning and evaluaton is different?
Huber loss function is widely used, because it combines the good properties of squared and absolute losses. Therefore, when I apply the penalized regressions, i.e. LASSO, Elastic net and Ridge, to ...
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Variable Length Input: How should variable-length input data be handled during the testing stage?
I have data that is sequential. Here, I am showing a toy example of my data in the following image:
I need to input the data into the model as groups of samples based on the class duration. To ...
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How to scale data for model retraining on production?
Let's say I have a basic regression model being used in production and now I want to implement periodical model retraining (i.e. once a month) where I take a batch of new data from last month and fit ...
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Is it correct to train and validate the model on F1-score metrics?
I am trying to do experiments on multiple data sets. Some are more imbalanced than others.
Now, in order to assure fair reporting, we compute F1-Score on test data. In most machine learning models, we ...
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What is meant by siamese network: train one network for each class or one network for all classes (example of training face recognition)
In siamese networks, the aim is to make closer the data from the same class and push far away the data coming from the different classes.
Suppose that we want a face identification system with 5 ...
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1
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How to split dataset into training and testing when intending to go into production?
I am doing a customer retention/churn prediction project where I have a dataset where each row comprises a customer's data/activity. Each column comprises the past 6 months of a customer's activity/...
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What's the official name of the "crop test"?
I call "crop test" or whether my model passed the "crop test" when I remove data from my dataset, conveniently before some events in the data to check whether the historical ...
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Negative KL divergence for train_test_split in sklearn for y_train and y_val
So, I am trying to understand if I have fair split of my train and val sets using train_test_split of sklearn, so I decided to run the KL divergence and JS div tests and I get the following results. ...
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711
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Train Test Validation standard split vs Cross Validation
This is a simple question… I am confused with the conceptual difference between a Train | Validation | Test split and K-fold validation.
In K-fold, I understood, We train and validate on everything ...
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Query regarding Deep learning model performance reporting
I am working on Human activity recognition via smart device sensors data by using deep learning. However, I am confused to report the results of my deep learning architecture. Therefore, I would like ...
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I've already used my entire dataset in a regression, should I not use that as a prediction model?
At the hospital I work at we were writing a paper on what variables about a patient predict whether they'll return for a follow-up visit. We included variables such as age, gender, distance from ...
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Validation loss stop decreasing in early stage of training
I'm training a LSTM+CRF model for sequence tagging. However, the validation loss of the model stops decreasing at early stage of the training. The below is the train losses and validation losses.
The ...
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500
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Time Series Forecasting Process With Regard to Training and Test Sets
I'm a bit confused about the process order in doing proper time series analysis/forecasting. Is it:
Stationary/seasonality checks, do any transformations required
Candidate model selection using ACF, ...
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Recurrent neural networks with loss of data
I want to train a recurrent neural network (RNN) for making predictions of some data.
I have 8 variable inputs and with them I have to make predictions of other two variables (outputs). I need RNN to ...
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How does glmnet in caret choose the values of lambda and how does it compute coefficients of the model?
I have a question that I've been struggling with. My students are asking me, but I can't figure it out myself.
When I train LASSO regression in R caret, I use the method "glmnet" and a grid ...
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Training in steps has any importance?
I'm trying to train a Siamese network for face Verification and eventually I came across the Contrastive Loss method for embedding vector distancing (kinda... I ...
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How to improve model generalization with small dataset?
I have a fairly small dataset (45 data points (i.e populations) where I took plant information. I'm running a random forest regression on my measurements and climate information to predict a feature, ...
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659
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About Epochs and how many of it?
I'm pritty new to the machine learning world, and I ws trying to figure out how many epochs should I run my training CNN model on the MNIST dataset (which has 60,000 training images and 10,000 ...
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