7
votes
Accepted
Is there a connection between the bias term in a linear regression model and the bias that can lead to under-fitting?
In machine learning, the term bias can refer to at least 2 related concepts
A (learnable) parameter of a model, such as a linear regression model, which allows you to learn a shifted function. For ...
- 41.1k
3
votes
Accepted
Is my GRU model under-fitting given this plot of the training and validation loss?
When ever you are buliding a ML Model don't take accuracy seriously(Mistake done by Netflix that cost them alot), you should try to get the hit scores as they will help you to know how many times your ...
- 70
2
votes
Accepted
TensorFlow estimator DNNClassifier fails to fit simple data
Normalise your inputs.
Neural networks work poorly outside of relatively small numerical ranges on input. An ideal range is for each feature to be drawn from $\mathcal{N}(0,1)$ i.e. a Normal ...
- 32.9k
1
vote
How do you interpret this train vs test accuracy scores? is the model under or over fitting?
For the Logistic Regression Classifier and SVC, the train_accuracy and test_accuracy are very similar; thus, there is no ...
- 987
1
vote
Accepted
Which model is better given their training and validation errors?
I would say that your intuition is correct: the model associated with the first plot is likely to generalise more than the one associated with the second plot.
In both cases, it doesn't seem that ...
- 41.1k
1
vote
Is my GRU model under-fitting given this plot of the training and validation loss?
You should at least crop the plots and add a legend. Maybe also provide some scores (accuracy, auc, whatever you're using).
Anyway, it doesn't look your model is underfitting, if it was you should ...
- 5,378
1
vote
Why would the application of boosting prevent underfitting?
It seems to me that the article is approaching it from the perspective of the base classifier. For example if the base classifier is a Decision Tree with a max depth of 1 (or any other severely ...
- 249
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