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
2
votes
2answers
30 views

How to build an overfitted network in order to increase performances

I am learning how to implement CNN, and searching on the internet I have found that a trick to design a good network is to first build it in such a way that it overfits, and then use regularization to ...
3
votes
1answer
32 views

Overfitting due to features correlating with training set generation rules

As background, I am using a Deep Neural Network built using Keras to classify inputs into 5 categories. The current structure of the network is: Input layer (~450 nodes) Dense layer (750 nodes) ...
1
vote
0answers
27 views

Conv Net Model is overfitting

So I made a convolution neural network to classify between different phonemes. My input datasets are a series of 0.4-second long spectrograms, the labels are each an individual phoneme that happens at ...
1
vote
1answer
16 views

On which step should use SMOTE technique for over sampling?

I want to use SMOTE technique for over sampling but I don't know on which step on pre-processing I should use it. My preprocessing steps are: Missing values Removing Outliers Smoothing Data Should ...
0
votes
0answers
28 views

One part of my loss function overfits. How do I fix this?

I am working on an object detection problem where the final loss that is being optimized is the sum of an L2 loss (for the error in the predicted w, h values), and three binary cross entropy losses (...
0
votes
1answer
49 views

Random Forest Overfitting, issues with mtry=1?

I am constructing what is known as an 'Expected Goals' model for football. This metric measures shot quality and a probability is assigned to a shot to achieve this, i.e. the chance a shot will be ...
3
votes
2answers
67 views

Am I overfitting my random forest model (more information in description)?

First off, sorry if this a novice question! Relatively new to all this stuff. Posted this in Stack Overflow and someone sent me here! Hope it's the right place. Anyway, I'm working with 22 datasets ...
0
votes
0answers
25 views

Neural Network is overfitting when using bigger dataset

I'm tring to train a model using CNN (supervised) to solve a binary classification problem. I have pretty big dataset containing 2 800 000 samples, each having 100+ features. Because training with ...
2
votes
2answers
40 views

If my model is overfitting the training dataset, does adding noise to training dataset help regularizing the machine learning model

I would like to know if this is a best practice or not. Can we add noise to the training data to help the model "fit less the training data"; as a result, hoping to generalize better on new unseen ...
0
votes
1answer
36 views

Decision tree and random forest over fitting

I am working on a real state data set to predict the price of buying a house in Dubai based on area, no.of bedrooms, number of baths and the town which the house is in. All variables are numerical ...
0
votes
1answer
32 views

Correctly evaluate model with oversampling and cross-validation

I'm dealing with a classic case of dataset with binary imbalanced target (event 3%, non event 97%). My idea is to apply some sort of sampling (over/under, SMOTE etc.) to address the issue. As I see, ...
2
votes
3answers
65 views

How to fix my high validation loss and inaccuracy

New to machine learning and tried to train my bird recognization model and found very high validation loss and inaccuracy. I'm using this dataset: http://www.vision.caltech.edu/visipedia/CUB-200-2011....
0
votes
0answers
9 views

Threshold for overfitted models

It's common knowledge in DS that overfitted models perform well on training data and poorly on test data. But how do you decide if a model is really overfitting? I have nowhere (books, online courses, ...
0
votes
0answers
11 views

Does a CNN fully memorize ground truth if it has more parameters than training pixels?

ResNet consists of 25M trainable parameters. If only 30% of 600 $512 \times 512$ images is annotated, there are $600 * 512 * 512 * ~0.3 = 47,185,920$ ground truth pixels. A parameter is a floating ...
1
vote
1answer
71 views

How to avoid overfitting in Reinforcement Learning

I have implemented a RL model based on Deep Q-Learning for learning how to play a 2D game, like the ones in the OpenAI Gym. For testing the model, unlike most people, I have chosen to evaluate its ...
1
vote
1answer
22 views

Minimum Possible Test MSE

I have a little confusion. What follows is from Introduction to Statistical Learning (2013) by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. My understanding of what is going ...
0
votes
0answers
8 views
0
votes
0answers
11 views

Overfitting collaborative filtering

So I want to know whether or not my models are overfitting or the difference between train and validation errors are decent. $L$: is the number of neighbors The first column is the train error ...
8
votes
3answers
143 views

Overfitting Question

Would you consider that overfitting?
1
vote
0answers
20 views

High Variance on CNN

I'm using a shallow CNN for my current project [this one]. I have a training dataset consisting of 1000 samples and a test dataset of 400 samples. I'm using the test dataset to choose the best ...
2
votes
1answer
39 views

Significant drop from validation accuracy to test accuracy

I am more familiar with classification tasks, though I have been working on a regression problem. I was given a large training dataset (>70k samples) and an independently collected test set (~2k). I ...
0
votes
0answers
22 views

CNN Architecture for Measuring Object Distances

I'm trying to use CNNs for infering object distances from an image. The input images correspond to states of a 2D game: Game states are not represented as images but as matrixes of observations. ...
1
vote
1answer
53 views

What is wrong with the below code? [closed]

I have been working on a project which i took from kaggle. I didn't get the result as mentioned in the website. What am I doing wrong here? ...
0
votes
0answers
20 views

At what value of Accuracy of train and test we can say Model is overfitting and Underfitting? [duplicate]

At what value of Accuracy of train and test we can say Model is overfitting and Under-fitting?
4
votes
1answer
112 views

Understanding why shuffling reduces weirdly the overfit

I am a student currently trying to create a classification model, however I am having difficulty understanding a weird overfitting problem. A dataset of about 30 000 entries, 30 features. The data is ...
4
votes
1answer
157 views

Interpretability of RMSE and R squared scores on cross validation

I'm working on a regression problem with 30k rows in my dataset, decided to use XGBoost mainly to avoid processing data for a quick primitive model. And i noticed upon doing cross-validation that ...
0
votes
0answers
25 views

How to gauge overfit with MLPClassifier and cross_val_score?

I'm learning sklearn. When using MLPClassifier.fit() and MLPClassifier.predict() I would ...
1
vote
1answer
39 views

Improving Accuracy of the Deep Learning Model

In my current project, I have only 647 rows (500 for training and 147 for testing) and I have applied the Keras Sequential model using the following code: ...
0
votes
0answers
46 views

My model is overfitting though i'v been using regularization techniques

I've been training an Xception model to recognize the disease of a plant from its leafs. So far i reached a training accuracy of 91% but the test accuracy is around 73%. So obviously my model is ...
0
votes
0answers
26 views

Autoencoder not overfitting data after large number of epochs and small number of samples

I am training a deep autoeocoder on numerical data, with python jupyter notebook. I have 17 samples, each with 534 values, and my auto encoder has all layers to 534, but even after 5,000 epochs, the ...
0
votes
0answers
24 views

Is the model over-fitting the data?

On the y-axis you've got RMSE and on the x-axis you've got the number of epochs. Then in blue, the validation error, in red the training error. What do you think is the optimal number of epochs ...
1
vote
1answer
227 views

How to know if a model is overfitting or underfitting by looking at graph

Just recently got my hands on tensorboard, but can you tell me what features should I look for in the graph (Accuracy and Validation Accuracy) And please do enlighten me about the concept of ...
1
vote
1answer
60 views

Overfitting CNN models

I tried to develop a number of CNN architectures to train on a 1000-point subset of the "cat-dog" Kaggle training set (meaning, by the way, that all 1000 data points were labeled). I used a 700-150-...
1
vote
3answers
107 views

What is the point of getting rid of overfitting?

I'm having trouble understanding why I would use dropout, regularization, data augmentation, etc to get rid of overfitting in the first place. I get that if your model is too large or data is too ...
0
votes
0answers
20 views

Getting the right architecture with dynamic feature selection

I am building a NN (with keras) to address a problem that is mappable to the following: Each sample is composed of ~250 features of which ~100 should be used to determine the importance of the other ...
2
votes
1answer
154 views

Which model to chose based on learning curve

I trained my model using different regression techniques, and I'm not sure which model to choose based on the learning curve. 1) Should I choose Lasso, since train and CV converge at the end 2) ...
0
votes
0answers
13 views

How to deal with a situation that the number of features in training dataset is larger than the number of training examples

I am playing a Kaggle competition, Don't Overfit Ⅱ And I am dealing with a situation that the number of features in training dataset is larger than the number of training examples, which has 250 ...
0
votes
0answers
13 views

Which accuracies to report in this case?

I am new to ML research and to writing ML paper. An ML research project resulted in a family of algorithms $A_i$. These algorithms transform certain type of data. This data is fed into a neural ...
0
votes
1answer
31 views

Having averaged trials which are less than the number of features

Suppose I have an experiment where I have 70 features and 48 samples. The target variable is binary (0,1) and the 48 samples are divided such that 24 of them correspond to outcome 1 and the other 24 ...
0
votes
1answer
238 views

My model accuracy doesn't change after first epoch

I've created a model to predict housing prices in LA, and what should be a simple regression problem, is giving me headache because the loss is just too big and my accuracy wont change. I've already ...
2
votes
0answers
111 views

Knowing when a GAN is overfitting (sequence classification study)

I have sequences of long, sparse 1_D vectors (3000 digits, made of of 0s and 1s) that I am trying to classify. I have previously implemented a simple CNN to classify them with relative success (with ...
2
votes
1answer
316 views

How to recognise when to stop training based on Overfitting/Underfitting?

I am trying to train a LSTM network, over a total of 200 epochs, with hidden layer size of 100 and 1 dense layer after the LSTM layer. I have used a batch size of 10 for the same. Basically, I am ...
2
votes
0answers
182 views

Overfitting in K-means

How do you test your results for overfitting in a k-means run? Some people have said use a training set. I have about 1500 records and about 20 fields.
0
votes
2answers
1k views

Over fitting in Transfer Learning with small dataset

I am using Transfer Learning to perform image classification. Base model used : Resnet50 using ImageNet dataset ...
3
votes
1answer
2k views

Validation loss increases and validation accuracy decreases

I have an issue with my model. I'm trying to use the most basic Conv1D model to analyze review data and output a rating of 1-5 class, therefore the loss is categorical_crossentropy. Model structure is ...
7
votes
2answers
2k 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
86 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. ...
3
votes
1answer
205 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 ...
2
votes
3answers
416 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
14 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). ...