Questions tagged [generalization]

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Will oversampling help with generalization (small imbalanced dataset)?

I have an imbalanced dataset (2:1 ratio) with about 60 patients and 80 features. I performed Recursive Feature Elimination (RFE) and stratified cross validation to reduce the features to 15 and I get ...
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1answer
26 views

Upper bound on size of sample set for decision trees

Say I have an instance space with 4 features and I know that a decision tree with 8 nodes can represent the target function I want to learn. I want to give an upper bound on the size of the sample set ...
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Why when my local cv of loss decreases, my leaderboard's loss increases?

I got a cv log_loss of 0.3025410331400577 when using 4-fold cross-validation and my leaderboard (with 30% of test dataset) got 0.26514. I further did feature engineering and added some features to the ...
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1answer
17 views

What is the form of data used for prediction with generalized stacking ensemble?

I am very confused as to how training data is split and on what data level 0 predictions are made when using generalized stacking. This question is similar to mine, but the answer is not sufficiently ...
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how to shuffle the data for model.fit with custom data generator?

So trainfiles is a list that contains the files' directory and name e.g. ['../train/1.npy' , '../train/2.npy'] and then I create a dataset as shown in the middle of the code then I apply it to model ...
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138 views

How can I measure the reliability of the specificity of a model with very small train, test, and validation datasets?

Stats newbie here. I have a small dataset of 646 samples that I've trained a reasonably performant model on (~99% test and val accuracy). To complicate things a little bit, the classes are somewhat ...
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1answer
21 views

Is generalizing a model, then removing the generalization good for FFNNs?

If one is training a basic FFNN (Feed-Forward Neural Network), one would apply regularizations like dropout, l1, l2 and gaussian noise, so that the model is robust and gives better results for unseen ...
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1answer
86 views

XGBoost: what to do when Kfold is not enough?

I have a dataset made of roughly 100 time-series and my final goal is to obtain a classification of each point (detection problem). To do so I have labels so I decided to use an XGB model to perform ...
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1answer
31 views

Why does this paper say that 0-1 loss is insensitive to scaling of weights in a neural network?

When discussing capacity control using norms of weights in a neural network,this paper says the following(see P4): Capacity control in terms of norm, when using a zero/one loss (i.e. counting ...
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59 views

Are there some research papers about text-to-set generation?

I have googled but find no results. Text-to-(word)set generation or sequence-to-(token)set generation. For example, input a text and then output the tags for this text: ...
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Research on explaining generalizability of deep learning methods

I've read a few classic papers on different architectures of deep CNNs used to solve varied image-related problems. I'm aware there's some paradox in how deep networks generalize well despite ...
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20 views

Learning discrete probability distribution that is parametrized by a set of real-valued parameters

Assume I have a discrete probability distribution defined over binary variables. This probability distribution is parametrized by a set of real-valued parameters, which all are contained in a segment, ...
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On design of the training set: conceptual question

I am curious to know how training data should be constructed so that it scales to examples that are not a part of the training data. For example, the problem that I am facing right now is in the ...
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920 views

Training on accurate data versus noisy data

I have data currently available that is very accurate and I would like to train my classification methods on this set of clean data to learn the important markers for distinguishing between classes. ...
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How is possible the result of GRU would other way around compared to LSTM while they were implemented samely?

Recently I crossed to a situation I can't figure it out why it happened. I applied six predictive models on a specific dataset as training-set and tried to predict the other similar dataset as an ...
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17 views

How much can be learned from a dataset?

Is there a way to quantify or characterize some upper limits for how much one can "learn" from a dataset? I got puzzled, when the neural state machine got me thinking components of AGI and lead me ...
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2answers
231 views

How is the equation for the relation between prediction error, bias, and variance defined?

I'm reading this article Understanding the BiasVariance Tradeoff. It mentioned: If we denote the variable we are trying to predict as $Y$ and our covariates as $X$, we may assume that there is a ...
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150 views

Generalization of RNN/LSTM/GRU… model

Given a time-series prediction with a Recurrent Neural Network (doesn't matter if LSTM/GRU/...), a forecast might look like this: to_predict (orange) was fed to the model, predicted (purple) is the ...
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60 views

Network either overfits or underfits, but never generalizes - what to do?

I have a simple network with 1st level an LSTM, dropout, fully-connected and softmax layers; loss is cross-entropy (four classes, well balanced). Sequence length to LSTM is 172 samples, data is z-...
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1answer
318 views

What is NLP technique to generalize manually created rules in text?

Let's say we have a free text containing key-value entities. Example: "... patient's tumour has width 6 cm and height 5 cm" Then an expert comes, marks it as important, thus we do have the rule for ...
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117 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 ...
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45 views

Why is it bad to use the same test dataset over and over again?

I am following this Google's series: Machine Learning Crash Course. On the chapter about generalisation, they make the following statement: Good performance on the test set is a useful indicator of ...
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740 views

The connection between optimization and generalization

Optimization algorithms such as gradient descent or particle swarm can find a minima in a function. On the other hand, learning methods such as back-prop define learning as an optimization problem ...
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1k views

The differences between SVM and Logistic Regression

I am reading about SVM and I've faced to the point that non-kernelized SVMs are nothing more than linear separators. Therefore, ...