Questions tagged [overfitting]

Question 1

Neural Network Beginner here. I am currently implementing a CNN on PyTorch for recognizing Japanese handwritten letters, which has 46 classes of outputs. I found a dataset on Kaggle https://www.kaggle....

Question 2

There is something I have an intuition on but my numerical toy examples do not confirm, and I really want to understand where is my mistake. I suppose that I have a random vector $X = (X_1, \cdots, ...

Question 3

The title is perhaps purposely provocative, but still reflects my ignorance. I am trying to understand carefully why, despite a very nice Bayesian interpretation, softmax might overfit, since I've ...

Question 4

How accurate are the estatimates of an order logit model with only 51 observations? Here is my stata output from the model:

Question 5

Learning about EM algorithms and finite mixture models and I've run into a particularly unintuitive problem. I'm trying to fit a finite mixture regression model on simulated data, where the true ...

Question 6

So I have a school project which is to train a CNN with our own architecture to be able to classify marine mammals with a minimum accuracy of 0.82 I have been trying a lot of things and different way ...

Question 7

Can AUC be used for model selection, and how can the excessive number of features/parameters be penalized in this case? In frequentist framework we have various model selection criteria, like AIC, BIC,...

Question 8

I am using a GridSearchCV to optimize some hyper parameters on a xgboost model. However, although the logloss (metric I am optimizing for) seems alright according to domain knowledge, the learning ...

Question 9

I'm working on fitting a random forest model using the caret library in R with a repeated cross-validation design to select hyperparameters. I've also experimented with adjusting the number of trees (...

Question 10

Assume you have training data $(x_1,y_1), \ldots, (x_n,y_n)$ and a relationship $y_i=f(x_i)+\epsilon_i$, where $\epsilon$ is a random variable. Assume you approximate $f$ with $\hat{f}$ using the ...

Question 11

This is sort-of a follow-up from my last question, except purely based on curiosity. I found different versions of similar bs="sz" models in ...

Question 12

I've been thinking about the use of cross-validation and hold-out sets and I don't really see the use of a randomly selected hold-out test set. I have to say, though, that when the hold-out is not ...

Question 13

Suppose I have a family of $N$ models for the same data, indexed by $n\in\{1,\dots,N\}$. And suppose that model $n\in\{1,\dots,N\}$ has log-likelihood given by: $$L(X_n \theta_n),$$ where $L:\mathbb{R}...

Question 14

I am training an MLP on a dataset with the number of features >> number of samples. For certain reasons, MLPs with at least one hidden layer is the only architecture I am considering. ...

Question 15

I have built an XGBoost model that performs rather weirdly across months... I trained the model on a heavily imbalanced dataset (1:40 000), which I undersampled to (1:500). The model performance (...

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Questions tagged [overfitting]

Potential CNN Overfitting Due to Limited Training Data

Generalization Error PCA (with closed formula) versus Ridge

How might softmax cause overfit in a neural model, even treated from a Bayesian perspective?

Inference validity of an ordered logit model with only 50 observations

Why do overfitted models in finite mixture regression sometimes have the smallest BIC despite the true number of components being selected frequently?

Overfitting problem in classification CNN

Number of features selection using AUC

Gridsearch results vs learning curve

How to reduce overfitting for a randomforest model even when cross validation is implemented?

Is there an one to one relationship between high bias and underfitting, and with high variance and overfitting?

How to identify problems with mgcv:gam(y ~ s(x) + s(x, fac, bs="sz"))? [closed]

The use of cross-validation and a hold-out set

Smooth AIC selection

Reducing MLP overfitting for feature importance

Model Performance Varying Greatly

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