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Questions tagged [overfitting]

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7
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2answers
285 views

Validation vs. test vs. training accuracy. Which one should I compare for claiming overfit?

I have read on the several answers here and on the Internet that cross-validation helps to indicate that if the model will generalize well or not and about overfitting. But I am confused that which ...
0
votes
0answers
14 views

Remedies to CNN-LSTM overfitting on relatively small image dataset

Notes Using a pretrained model, trying data augmentation (not possible knowing nature of images, lowering number of parameters in the network, all didn't help) Context I have a sequence of images. ...
0
votes
0answers
32 views

Is this an overfitting?

I'm trying to learn a binary classifier (keras, fully connected NN with 1-4 hidden layers, 16-1024 neurons in each) on pretty skewed dataset of ~130 thousands examples with only ~6% of positives. ...
0
votes
1answer
82 views

Overfitting - how to detect it and reduce it?

I have a side project where I am doing credit scoring using R (sample size around 16k for train data and 4k for test data, and also another two 20k data batches for out-of-time validation) with ...
1
vote
2answers
72 views

SciKit-Learn Decision Tree Overfitting

I'm pursuing a computer science minor at my university, and one class I'm in is Machine Learning. We have a project to utilize a few algorithms we have learned so far. I've been using SciKit-Learn to ...
0
votes
0answers
10 views

How to apply curve fitting on both completed and active data?

Suppose we have a set of part failure times on which a specific curve (e.g. gamma distribution) is already fitted. Then another set of times are given where the part is still active (not failed yet). ...
1
vote
0answers
50 views

Network either overfits or underfits, but never generalizes - what to do?

I have a simple network with 1st level an LSTM, dropout, fully-connected and softmax layers; loss is cross-entropy (four classes, well balanced). Sequence length to LSTM is 172 samples, data is z-...
1
vote
2answers
36 views

Is there a disadvantage to letting a model train for a large number of epochs?

I created a model to solve a time series forecasting problem. I had a limited amount of time series with which I could train the model therefore I decided to augment the data. The data augmentation ...
1
vote
2answers
45 views

Is it bad to have a large gap between training loss and validation loss?

Say my training loss is 0.5 and my validation loss is 2.5 (both have stopped decreasing, validation loss never increased). I am clearly overfitting. If I add regularization, my training loss becomes 1 ...
1
vote
1answer
64 views

Retrain image classifier using MobileNet v2

I am using my own dataset to retrain mobilenet_v2_100_224 model, I currently have 4 classes where each class have more than 100 images still I'm observing overfitting even though I've used ...
2
votes
1answer
41 views

Bias-variance tradeoff in practice (CNN)

I first trained a CNN on my dataset and got a loss plot that looks somewhat like this: Orange is training loss, blue is dev loss. As you can see, the training loss is lower than the dev loss, so I ...
1
vote
2answers
73 views

How can I know if my NN TensorFlow model is overfitted or not?

I am new with TensorFlow (Python) and I can not juge my obtained results in terms of training and testing accuracy I am using the GradientDescentOptimizer with a learning coeff equal to 10^(-4) and ...
0
votes
1answer
26 views

Difference in labelling and normalizing train/test data

I am working on a dataset comprised of almost 17000 data points. Since it's a financial dataset and the components are many different companies, I need necessarily to split it by date. Therefore, ...
0
votes
1answer
21 views

Does running the script of train_test_split several times with varying test size in sklearn cause data leakage?

I ran a script of ridge and lasso regression twice with and without pca. Both times i got an okay R^2. but when i changed the train_test_split test size from 20 to 30%. My model started to over fit. ...
1
vote
2answers
45 views

How can someone avoid over fitting or data leak in ridge and lasso regression when the training score is high and test score is low?

I used the code provided here: https://towardsdatascience.com/ridge-and-lasso-regression-a-complete-guide-with-python-scikit-learn-e20e34bcbf0b The only difference is that i used StandardScalar on my ...
0
votes
0answers
41 views

Are my Random Forest Classifier and Regressors overfitting?? I have CV and learning curves!

I seem to be getting great results from logistic regression with RFE and random forest feature importances in support, but there's been a suggestion of overfitting and when I run learning curves the ...
4
votes
3answers
155 views

Is Overfitting always bad?

I have a data set of total 8000 sound samples. These are the results of my multi layer neural network, binary classifier: ...
2
votes
0answers
33 views

How to improve a model with a high cross validation score yet with low accuracy on unseen data?

I have a model that is based on an experiment collected on 100 subjects. We are testing the model as follows: Record raw data from the subjects For each subject, compute the feature from the raw data ...
-1
votes
0answers
45 views

How to fit a Random Forest with a very small amount of data?

I am working on a Signal Processing project in the Bio-Medical domain. I have to implement a Random Forest Classifier to classify Lung X-Ray parameters in terms of Tuberculosis. The data is in the ...
-1
votes
0answers
85 views

Controlling how much a VAE overfits

I want to make my VAE overfit to the training sample to some degree. What is the best to way to control it? Weighting the KL divergence loss term, which basically becomes beta-VAE if I'm not wrong? ...
0
votes
1answer
17 views

What are some good design practices for creating/improving a CNN?

Recently I've been working on a mini side project in detecting age off of facial images. Aside from mistakes, I have made decent progress in creating my model. ...
1
vote
2answers
44 views

What are the possible approaches to fixing Overfitting on a CNN?

Currently I am trying to make a cnn that would allow for age detection on facial images. My dataset has the following shape where the images are grayscale. ...
-1
votes
0answers
5 views

When is a weather forecast 'in-sample'?

I've got some weather forecast data and I want to split it into a sample for analysis (in-sample) and a sample for testing (out-of-sample), to avoid over-fitting to the data. I made the choice to ...
1
vote
1answer
45 views

Not sure if over-fitting

I trained the data this way : There are four classes , the data distributed evenly (same amount of labels). Used min_max_scaler Used train_test_split(X,y,test_size=0.3,random_state=42,stratify=y) ...
0
votes
1answer
112 views

Check Overfitting in CNN

I am kind of new to NLP and text classification with Convolutional Neural Nets, and I have trained my first models quite recently. I am a little bit concerned with overfitting. I am doing multilabel ...
0
votes
1answer
16 views

Décision tree, How to see under/over fitting with just looking at the leafs?

My question is: how with just looking at the leafs of a decision tree could you tell if the model is under/over-fitting? Any sort of advice will be helpful.
3
votes
1answer
580 views

overfit a Random Forest

I am trying to overfit to the maximum a random forest classifier using scikit-learn to make some tests. Does somebody know what hyperparameters I can tune to do that? Or does somebody know which ...
0
votes
2answers
64 views

Is a Neural Network with 20 times the number of input neurons (on hidden layers) guaranteed to overfit? When is this not so?

I'm aware of the problem of over-fitting. One way to describe it is your Neural Network learning your training data to a high accuracy and performing poorly (generalizing) on new data. Was wondering ...
2
votes
1answer
816 views

Early stopping and final Loss or weights of models

In a deep model, I used the Early stopping technique as below in Keras: ...
2
votes
1answer
2k views

When should one use L1, L2 regularization instead of dropout layer, given that both serve same purpose of reducing overfitting?

In Keras, there are 2 methods to reduce over-fitting. L1,L2 regularization or dropout layer. https://keras.io/regularizers/ https://keras.io/layers/core/#dropout What are some situations to use L1,...
0
votes
1answer
46 views

How to do k-fold validation with classifiers?

I want to cross-validate a model that plays the card game below (see image). I trained the model on a dataset of 1000 games, with the goal to maximise the profit of each game. It works great on the ...
5
votes
1answer
40 views

How can I prevent this model to learn more(less) :)))

As you can see, GradientBoostingClassifier overfit with more training example. These are my parameter for the model: {'learning_rate': 0.1, 'loss': 'deviance', 'max_depth': 6, 'max_features': 0.3, '...
1
vote
1answer
226 views

What is Dropout in FC layer?

I know the purpose of dropout is to avoid overfitting by deactivating some neurons. However, I am interested in how it's done, ie: Math behind it, or intuition about why it works.
2
votes
0answers
39 views

Is my model overfitting when I add new features?

I'm working on simple 2-class classification problem. Nearly all features we have used (except one) are about the same for both classes: A random forest classifier confirms that one feature has an "...
2
votes
1answer
73 views

Convolution neural network with 11 million parameters unable to overfit on 100 image samples

I have been trying to do some sort of image enhancement on grayscale images. I have used both pixel wise loss and perceptual loss (perceptual loss uses classifier between 2 classes trained on the same ...
4
votes
2answers
73 views

overfit random walk using ANN in Keras

I am trying to build a neural net that will overfit random walk path. So, far I wasn't able to get a neural net that we shatter/overfit. I was wondering which parameters I should explore, or which ...
1
vote
2answers
231 views

Minimum number of samples to train XGBoost without overfitting

When using Neural Networks for image processing I learned a rule of thumb: to avoid overfitting, supply at least 10 training examples for every neuron. Is there a similar rule of thumb for XGBoost, ...
2
votes
1answer
48 views

Overfitted model produces similar AUC on test set, so which model do I go with?

I was trying to compare the effect of running GridSearchCV on a dataset which was oversampled prior and oversampled after the training folds are selected. The oversampling approach I used was random ...
3
votes
1answer
796 views

Too much inputs = overfitting?

First question : can I mix different sorts of inputs types for example, height and age (of course my inputs are normalized)? in general, can we mix different types of inputs in a neural network ? ...
1
vote
2answers
175 views

Overfitting problem in model

I am making a project on prediction cars price given its features. I was able to scrape over 13000 examples. After cleaning and manipulating the data, I left with a little more than 11000 examples, I ...
3
votes
2answers
564 views

Why validation loss worsens while precision/recall continue to improve?

I'm training a neural network on 'easy' dataset with ~15k examples. Network overfits pretty fast. The thing I cannot understand that after 5th epoch validation loss is starting to worsen, while ...
1
vote
1answer
53 views

Loss for CNN decreases and settles but training accuracy does not improve

I am training a CNN with 2 conv layers 2 Relu and max pooling and 2 FC layers the last of which has only 2 units since it's a binary classification problem. The images are spatio-temporal continuous, ...
3
votes
4answers
2k views

In which epoch should i stop the training to avoid overfitting

I'm working on an age estimation project trying to classify a given face in a predefined age range. For that purpose I'm training a deep NN using the keras library. The accuracy for the training and ...
2
votes
2answers
143 views

Is this an over-fitting case?

I'm a new programmer and this is my first ever neural network for real world application. Here is the deal, I'm using a top-less pre-trained VGG-16 with some dense layers on top of it.(for image ...
1
vote
2answers
83 views

problems during training a MLP type of network

I trained a neural network model, a MLP type of network, where the first several layers are 1-D convolution for processing sequence type of input. However, the training process looks like as follows, ...
2
votes
3answers
102 views

Is this clear overfitting?

The orange curve is train accuracy and blue is validation accuracy. Is this clear overfitting or should I let it run for more epochs? With custom dataset (1D data with 70 features) I trained a 2 ...
4
votes
1answer
2k views

Accuracy and loss don't change in CNN. Is it over-fitting?

My task is to perform classify news articles as Interesting [1] or Uninteresting [0]. My training set has 4053 articles out of which 179 are Interesting. The validation set has 664 articles out of ...
5
votes
1answer
959 views

Dropout vs weight decay

Dropout and weight decay are both regularization techniques. From my experience, dropout has been more widely used in the last few years. Are there scenarios where weight decay shines more than ...
2
votes
3answers
4k views

How to improve loss and avoid overfitting

I'm trying to build a 2 class image classifier using the architecture suggested in first part of this blog https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data....
3
votes
1answer
81 views

Significant overfitting with CV

I working on a binary classification task. The dataset is quite small ~1800 rows and ~60 columns. There are no duplicates in the rows. I am comparing different classifiers amongst the canonical ones: ...