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Questions tagged [overfitting]

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1answer
18 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 ...
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2answers
56 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 ...
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1answer
18 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, ...
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1answer
15 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. ...
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2answers
32 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 ...
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0answers
28 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 ...
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3answers
100 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: ...
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0answers
19 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 ...
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0answers
17 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 ...
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0answers
19 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? ...
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1answer
16 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. ...
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2answers
33 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. ...
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0answers
4 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 ...
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1answer
42 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
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1answer
59 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
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1answer
15 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.
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0answers
39 views

How to deal with overfitting in Gradient boosting classification algorithm?

I am training my model on Gradient boosting algorithm with parameters as follows: learning rate: 0.1 number of iterations: 100 depth of tree: 12 I am not getting the output for cross-validation to ...
3
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1answer
401 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 ...
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0answers
38 views

Learning a non-linear mapping using LSTM units: encountering overfitting

I am trying to use a recurrent neural network to perform sensor fusion for an inertial measurement unit. IMUs are commonly used in conjunction with a Kalman filter (KF), which performs both fusion of ...
0
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2answers
58 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
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1answer
466 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
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1answer
910 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,...
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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 ...
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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
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1answer
203 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.
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0answers
37 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
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1answer
63 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 ...
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2answers
69 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 ...
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0answers
48 views

Generate music using LSTM - language model overfitting problem

I read this blog post https://towardsdatascience.com/how-to-generate-music-using-a-lstm-neural-network-in-keras-68786834d4c5 about generate music using LSTMs from midi data. The model is based on the ...
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2answers
159 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, ...
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0answers
122 views

Two different approaches of oversampling data with GridSearchCV leads to similar test results

I was trying to compare two approaches to optimal selection of hyperparameters based on two approaches: 1) Wrong Approach: Oversampling before GridSearch CV This can lead to bleeding of data (that ...
2
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1answer
45 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
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1answer
541 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 ? ...
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0answers
26 views

Problem on the implementation of linear regression using tensorflow

I've implemented a basic linear regression with tensorflow and the bash gradient descent. It work perfectly but i wanted to see the regularization method on a over-fitted system. So I set a high-...
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2answers
159 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
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2answers
335 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
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1answer
47 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, ...
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4answers
1k 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
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2answers
101 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 ...
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2answers
69 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, ...
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3answers
99 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
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1answer
1k 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
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1answer
769 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 ...
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3answers
2k 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
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1answer
68 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: ...
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4answers
1k views

High model accuracy vs very low validation accuarcy

I'm building a sentiment analysis program in python using Keras Sequential model for deep learning my data is 20,000 tweets: positive tweets: 9152 tweets negative tweets: 10849 tweets I wrote a ...
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2answers
296 views

Detecting over fitting of SVM/SVC

I am using 3-fold cross validation and a grid search of the C and gamma parameters for a SVC using the RBF kernel I have achieved a classification score of 84%. When testing against live data the ...
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0answers
147 views

Keras - Why Validation data produce good results, while unseen data is performing poorly

I've built a feedforward net that predicts 2 classes (0,1). I've used the validation_split attribute like so: ...
1
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1answer
512 views

Configuring Incremental XGBoost model

I have a large dataset which can't be loaded in memory, hence I decided to use incremental learning using Xgboost. What I have done currently is: Tuned num_boosting_rounds using a chunk of data Set ...
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2answers
2k views

Is a 100% model accuracy on out-of-sample data overfitting?

I have just completed the machine learning for R course on cognitiveclass.ai and have begun experimenting with randomforests. I have made a model by using the "randomForest" library in R. The model ...