Data Science Stack Exchange
Data Science Stack Exchange

Questions tagged [generalization]

47 questions
12
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2 answers

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, is the only difference between an SVM and logistic regression the criterium to choose the boundary? Apparently, SVM…
David Masip
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2 answers

High accuracy on test-set, what could go wrong?

You are given a pre-trained binary ML classification model with 99% accuracy on the test-set (assume the customer required 95% and that the test-set is balanced). We would like to deploy our model in production. What could go wrong? How would you…
CodeHoarder
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7
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2 answers

Multilabel classification: Choosing threshold

I'm creating a multilabel classification approach based on sentence embeddings applied to text taken from a chatbot. We have the following: a training dataset of 2,500 lines, where each line is a sentence associated with a particular label (the…
LasiusMind
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6
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1 answer

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. But in the future, my trained classifiers will not…
Mathews24
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5
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1 answer

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 application of identifying or distinguishing the…
Sm1
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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 and are used to learn weights in deep neural…
PickleRick
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1 answer

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 finding the same entity in new, different…
ludgo
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2
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1 answer

Does convergence equal learning in Deep Q-learning?

In my current research project I'm using the Deep Q-learning algorithm. The setup is as follows: I'm training the model (using Deep Q-learning) on a static dataset made up of experiences extracted from N levels of a given game. Then, I want to use…
Aeryan
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2
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Multilabel Classification - Overfitting?

My task is the following: To input drug combinations and output renal failure-related symptoms from the drug combinations. Both the drug combinations and renal-failure related symptoms are represented as one-hot encoded (for example, someone getting…
shepan6
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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 errors) requires us in addition to account for scaling…
j200932
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2
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1 answer

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 the detection over some features that I have…
FrankBool
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How to train a neural network to generalize out-of-distribution (e.g., sin function extrapolation) without data leakage?

I am trying to train a neural network to approximate the sin(x) function, but I want it to generalize outside the range of the training data. Specifically, I train the network on x values within [-π, π] and test it on a disjoint range, such as [π,…
Amirhossein Rezaei
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Which type of models generalize better, generative or discriminative models?

In NLP, which type of models (generative or discriminative) is more sensitive to the amount of data to generalize better? references? This is related to the way those two types capture the data probability (join-prob. vs conditional prob.)?
Minions
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Graph Neural Network fails at generalizing on unseen graph topologies

I'm using PytorchGeometric to train a graph convolutional network for regression over nodes problem (the graph models physical phenomena in the network of sensors; the network of sensors is actually the network of measurements distributed across the…
sesli
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Generalization error problem on training set

Training data: $\mathcal {T} =\{(2,1),(3,2),(4,6),(0,0),(1,1)\}$ you already computed a predictor for the output using linear regression by least squares, where you used the first 3 samples as training samples: $f(X) = -4.5  +  2.5X$ Approximate the…
todapod264
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