Questions tagged [underfitting]
For questions related to the concept of underfitting in machine learning, which occurs when a machine learning model is not able to learn.
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How do you interpret this train vs test accuracy scores? is the model under or over fitting?
What does this difference in train and test accuracy mean?
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Why would the application of boosting prevent underfitting?
"Why would the application of boosting prevent underfitting?"
I read in some paper that applying boosting would prevent you from underfitting. Why is that?
Source:
https://www.cs.cornell.edu/...
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If the model always underfits, do I really need a larger model?
I train my neural network on random points generated for a data set that theoretically consists of approximately $1.8 * 10^{39}$ elements. I sample (generate) tens of thousands of random points on ...
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Is there a connection between the bias term in a linear regression model and the bias that can lead to under-fitting?
Here is a linear regression model
$$y = mx + b,$$
where $b$ is known as $y$-intercept, but also known as the bias [1], $m$ is the slope, and $x$ is the feature vector.
As I understood, in machine ...
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Identifying if a model is over or under-fitting via graphs
I am working on a Neural Network and have plotted the performance of my model. However the plots seem not to fit the "trends" (which help you identify the issue with your model) presented in ...
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Underfitting a single batch: Can't cause autoencoder to overfit multi-sample batches of 1d data. How to debug?
TL;DR
I am unable to overfit batches with multiple samples using autoencoder.
Fully connected decoder seems to handle more samples per batch than conv decoder, but then also fails when number of ...
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Which model is better given their training and validation errors?
Below you have the plots of the training and validation errors for two different models. Both plots show the RMSE values for the validation dataset versus the number of training epochs. It is observed ...
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Is my GRU model under-fitting given this plot of the training and validation loss?
I was running my gated recurrent unit (GRU) model. I wanted to get an opinion if my loss and validation loss graph is good or not, since I'm new to this and don't really know if that is considered ...
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Are there principled ways of tuning a neural network in case of overfitting and underfitting?
Whenever I tune my neural network, I usually take the common approach of defining some layers with some neurons.
If it overfits, I reduce the layers, neurons, add dropout, utilize regularisation.
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TensorFlow estimator DNNClassifier fails to fit simple data
The ready-to-use DNNClassifier in tf.estimator seems not able to fit these data:
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