Questions tagged [overfitting]

Modeling error (especially sampling error) instead of replicable and informative relationships among variables improves model fit statistics, but reduces parsimony, and worsens explanatory and predictive validity.

Filter by
Sorted by
Tagged with
0
votes
2answers
26 views
0
votes
2answers
59 views

How can I get the type of fitting in this curve?

Does that overfitting ? How can I interpret the curve ?
1
vote
0answers
10 views

Overfitting reason in 2-stage model

I'm trying to build an entity matching model. There are 2 kinds of features - binary (0/1) and text features. Initially I made a deep learning model that uses character level embeddings of some of the ...
1
vote
1answer
42 views

How do I know if this model is overfitting?

This is my example R script for a decision tree: ...
1
vote
0answers
31 views

Automate detection of overfitting models based on autoML libraries

I'm trying to use machine learning to impute missing data in series using some auto-ML libraries in python (so far : dabl, FLAML, auto-sklearn and AutoKeras). I know the way to detect overfitting in a ...
4
votes
1answer
98 views

What can I do when my test and validation scores are good, but the submission is terrible?

This is a very broad question, I understand and I'm totally fine if someone believes it's not appropriate to do it. But it's killing me not to understand this... Here's the thing, I'm doing a machine ...
0
votes
0answers
8 views

selection of loss function to avoid overfitting by autoencoder in prediction a figure with a sharp rise

I have to select the loss function to avoid overfitting by autoencoder in prediction of this figure that has a sharp raise, I would like to find how to avoid overfitting by autoencoder in prediction a ...
1
vote
1answer
13 views

Can Online DQN model overfit?

I am new in the area of RL and currently trying to train an online DQN model. Can an online model overfit since its always learning? and how can I tell if that happens?
0
votes
0answers
16 views

Data augmentation within epochs vs across epochs

Usually in deep learning data augmentation is applied by creating a new augmented version of each training sample for each epoch. Therefore the amount of training samples for each epoch stays the same ...
0
votes
0answers
12 views

Model variance increases during training

I trained a regression model with lightgbm and the learning curve doesn't look good: The model variance increases during training, which shows a kind of overfitting. Now, I tried many ways to fix ...
0
votes
0answers
19 views

Possible fixes for an overfitting random forest regressor?

I'm fitting a random forest regressor on my dataset (do note its not a classifer but a regressor since the target is a continuous variable) through a grid search cross-validation in sklearn. The ...
1
vote
4answers
105 views

What do "Under fitting" and "Over fitting" really mean? They have never been clearly defined

I am always getting lost when dealing with these terms. Especially being asked questions about the relationship such as underfitting-high bias (low variance) or overfitting-high variance (low bias). ...
0
votes
0answers
21 views

Can there be scenarios where an overfitted model in machine learning cannot be generalized?

Is it always possible to generalize an overfitted model? I know there are ways to handle overfitting, but can there be scenarios where overfitting cannot be handled in machine learning?
1
vote
0answers
13 views

Rule of Thumb for number of observations required to train a model with n independent variables?

I am aware adding more features to a model leads to overfitting of a model. Is there a rule of thumb for minimum number of rows required to build a model with n features in order to build a ...
0
votes
1answer
54 views

Learning curves

I am working on a multiclass classification problem. I want to know whether my model is overfitting or underfitting. I am learning how to plot learning curves and have 4 doubts. 1.) Is the ordering of ...
1
vote
1answer
44 views

Perfect scores for multiclass classification

I am working on a multiclass classification problem with 3 (1, 2, 3) classes being perfectly distributed. (70 instances of each class resulting in (210, 8) dataframe). Now my data has all the 3 ...
2
votes
0answers
22 views

Building machine learning models whilst penalizing them for complexity

I come from a predictive modelling background, where it's common to use differential equations to model physical or chemical or biological processes. Commonly to avoid overfitting people use AIC and ...
1
vote
1answer
32 views

Will repeatedly fine-tuning on new data cause overfitting?

I have a binary classification model which I have trained on a training set. On the validation set its accuracy is ~85%. I set up early stopping which ended training when validation loss increased. ...
2
votes
1answer
80 views

Is it possible to use a Neural Network to interpolate data?

I am completely new to Artificial intelligence and Neural Networks. I am currently working on a plasma physics simulation project which requires a very high resolution data set. We currently have the ...
2
votes
0answers
21 views

When to stop the final model training?

Let's say I'm participating in a Kaggle image recognition competition. Firstly, I create a train/validation split and find the good hyperparameters for my model. Here the stopping criterion is when ...
0
votes
0answers
16 views

Systematically finding a CNN architecture?

I am trying to train a classifier from 25k images and 7k classes. Seems like my model overfits just after 3 epochs. I have tried to reduce the model complexity and increase the weight decay but still, ...
1
vote
0answers
23 views

Random Forest overfitting with `n_estimators=1`, `max_depth=1 and `max_features =1`

I am trying to stop my RF from overfitting. I am using time series data with 1 day time lag, to predict the current price. I am using this function to shift my independent features back 1 day: ...
4
votes
2answers
226 views

Overfitting in active learning

How can I make sure that the initial model trained on a small dataset will not suffer from overfitting before applying the active learning sampling techniques? because I will use this model to select ...
0
votes
0answers
39 views

Is my CNN model overfitting or underfitting?

I would like to be sure of whether the model is overfitting or undercutting. Being new to this, is there any specific point to identify when to stop the training process. Any help in this regard would ...
2
votes
1answer
54 views

How to analyze neural network quality in case of overfitting?

I have a Keras neural network that has images both as input and reference data. My network demonstrates overfitting (for example, train accuracy is about 80% but test accuracy is only up to 70%) due ...
1
vote
0answers
84 views

CNN + LSTM model for images performs poorly on validation data set

My training and loss curves look like below and yes, similar graphs have received comments like "Classic overfitting" and I get it. My model looks like below, ...
1
vote
0answers
73 views

XGBoost regressor hyperparameter tuning with hyperopt leads to overfit

Using hyperopt to hyperparameter tuning on XGBoost regressor, I am receiving overfiting on the train set. There is any suggestion how to solve it ? I have used cross validation with ...
0
votes
0answers
23 views

How to improve SGD classifier performance?

I am having some issues with SGD to run with a sample of approx 5000 obs for a classification problem with imbalance. I am doing ...
0
votes
1answer
281 views

How to perform Multi-Label Image Classification with EfficientNet

Problem My goal is to perform multi-label image classification with EfficientNet. It should take a picture as input and e.g. tell the user that it sees a person AND a dog on the picture, meaning the ...
0
votes
0answers
48 views

Unet Overfitting for binary segmentation of fake images

I am working on a project where I am trying to detect and localize forgeries in images. I am using the CASIA v2 dataset and using Unet model for the task. I have the binary masks of all the images in ...
1
vote
0answers
24 views

Understanding model's learning curves

I'm trying to train a Lane Detection CNN called PINet on a proprietary dataset. Below are some of the important configuration values: Batch size: 6 Optimizer: Adam Learning rate: High of 1e-4 and Low ...
1
vote
2answers
50 views

ideal algorithms to demonstrate overfitting or underfitting

When one tries to look up concepts such as overfitting and underfitting, the most common thing that pops up is polynomial regression. Why is polynomial regression often used to demonstrate these ...
0
votes
1answer
22 views

Accuracy on Validation and Test set, Overfit?

Just a quick question, I am building a ML model right now however I am receiving very similar (72.2 and 72.4 for example)% for both Accuracy and F1-Score on my Validation Dataset and my unseen Test ...
0
votes
1answer
16 views

Avoiding overfitting in unsupervised ML

I am using a unsupervised pattern matching approach to create a trade strategy. I use the output of the pattern matched results to decide whether to enter a trade or not. For deciding the best pattern ...
0
votes
0answers
37 views

How many epochs should all the data be trained on after training with validation finds when validation and training diverge?

One uses and train/test split to use their training data to get an idea of how many epochs to train with. If the validation accuracy starts going down while the training accuracy is still going up, ...
1
vote
0answers
118 views

BERT MLM overfitting [closed]

We are training the BERT model on masked language modeling task for the Russian Language. Our dataset consists of 60 mln texts with (128 tokens for each text) from online social networks, ...
0
votes
1answer
29 views

Performance metrics changing significantly based on batch size

I am working on a binary classification problem where there is significant class imbalance (minority class makes up nearly 10%). The dataset has ~15,000 observations and I have split this in to a ...
1
vote
2answers
46 views

dataset split for image classification

I am trying to do image classification for 14 categories (around 1000 images for each cat). And i initially created two folders for training and validation. In this case, do I still need to set a ...
0
votes
0answers
26 views

How can I prevent overfitting?

hope to find you well ! I am trying to build a model to classiffy customers with propensity to buy, but i cannot get rid of overfitting! My approach is the following: I have created the train dataset ...
0
votes
0answers
16 views

XGBoost: is increasing gamma same as feature selection by average gain?

Since gamma limits splits unless they meet a minimum gain threshold, isn't that the same thing as removing features that have low average gain? Both will results in splits with higher average gains. I ...
0
votes
0answers
25 views

What would cause regression to focus on a few values?

Hi I have a neural network for a regression problem but the results seem to be heavily focused on a few values. I added an image, y axis is predicted values and x axis is real values. What could be ...
0
votes
0answers
32 views

Validation accuracy does not increase for binary classification using GNN

I am trying to perform graph classification with GINConv model of GNN. I have tried everything from varying dropouts to weight decay (for L2 regularization), learning rate (1e-6 to 1e-3), batch ...
0
votes
0answers
9 views

Avoid overfitting in 3 layer neural net for cifar10

I made a neural net with 1 conv and 2 fc layers. I am getting 80-90% accuracy on train set but 60% on train set. I used batchnorm layer too but it didn't remove the overfitting. How can I remove it ...
1
vote
2answers
116 views

Autenocoder and anomaly detection task

I'm trying to create an autoencoder for the anomaly detection task, but I'm noticing that even if it performs very well on the training set, it starts to stop recreating half of the test set. I tried ...
0
votes
0answers
18 views

TF Estimator stop when overfitting?

With TF 1.x is there a way to stop training, when evaluation loss starts increasing? Or is there another way to detect overlearning? I know about the stop_if_no_decrease_hook, but it doesn't consider ...
0
votes
1answer
89 views

Precision, recall and accuracy metrics significantly different between training/validation and actual predictions

I have two sequential models built with Keras that train on data from a CSV file. This is how they are built ...
0
votes
0answers
21 views

Different names for overfitting

If I understand overfitting is simply when our training loss is lower than our test loss. Now let's say we have 2 situations Where your test loss actually starts going up after a certain number of ...
0
votes
0answers
23 views

Neural Net that memorizes what it sees in order?

I'm sorry for this weird question, I know ML is about generalization but I have a specific use case where I'd like to build a neural network or really just a matrix, that memorizes everything it sees ...
1
vote
0answers
19 views

Normal distribution of errors

I'm trying to project lifetime of customers in my company, based on various parameters I've reached a 64% correlation so far, between the valid and prediction data I'm using light GBM regressor I did ...
0
votes
0answers
20 views

Data scaling for convolutional neural networks and other issues

My project involves deep learning on physiological data gathered from a wearable device. I aim to evaluate the potential CNN usefulness in classifying data collected from the wearable. The wearable ...

1
2 3 4 5 6