Questions tagged [theory]
Theory relates to theoretical questions regarding data science and machine learning.
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How to compensate for self-induced bias
Scenario:
I develop a model which forecasts the likely sales success of a particular enquiry based on outcomes of past similar enquiries.
I then assign this likelihood score to new enquiries when they ...
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Is there any formula for finding the smallest no. of chapters needed to be learnt for an exam/test, based on the number of questions they can ask?
I understand that this is a highly unconventional and specific question, so bear with me. Also, this is my first time using the site, so be a little lenient with the downvotes.
I want to know if there ...
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If a machine learning model can be trained to obtain B from A, and another to obtain C from B, could a final model obtain A from C?
I've recently been working on a regression model based on some physics to obtain some numbers C from a set of features A, although with little success. Knowing that the formula that relates A to C ...
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How to scale a subset of data with respect to the entire dataset
I am developing a financial time-series prediction model using sklearn using StandardScaler for scaling purposes. I train a model, and then use the model regularly ...
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Minor error in Ian Goodfellow's GAN optimality proof
I've been thinking of a part of the proof of the optimality of GANs from the original paper, and I can't manage to solve what seems to be an error. The paper states that the maximum of the function $y ...
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How do we derive our loss function from the gradient objective?
I've been dwelling through RL theory and practice and one particular part I find hard to properly understand is the relation between the practical loss function and ...
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Learning the Average of a 0/1 Dependent Variable
uppose I have a matrix 𝑋 and a dependent vector 𝑦 whose entries are each
in {0,1}
dependent on the corresponding row of 𝑋
Given this dataset, I'd like to learn a model, so that given some other ...
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Geometric Deep Learning - G-Smoothing operator on polynomials
(Note: My question resolves about a problem stated in the following lecture video: https://youtu.be/ERL17gbbSwo?t=413
Hi,
I hope this is the right forum for these kind of questions. I'm currently ...
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Creating a map between N images and N labels using CNN
I have seen classification CNNs that train with numerous images for a subset of labels (i.e. Number of images >> Number of labels), however, is it still possible to use CNNs when the number of ...
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Time series test data dilema
I’m trying to build a model to predict the amount of sales of a product for the next few days
This question is about whether or not I should use the tail of the serie as the test set and train models ...
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Proof of GOSS algorithm in lightGBM paper
In the LightGBM paper the authors make use of a newly developed sampling method GOSS to reduce the number of data instances needed for finding the best split of a ...
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Lasso (or Ridge) vs Bayesian MAP
This is the first time I have posted here. I am looking for some feedback or perspective on this question.
To make it simple, let's just talk about linear models. We know the MLE solution for the $l_1$...
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How to use the eval set in catboost appropriately?
Let's say you have a dataset, and you split it into 80% training and 20% testing. Naturally, you want to find the optimal hyperparameters for your model, so with the training set, you plan to do cross ...
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Would all classification models perform similarly in a theoretical and ideal scenario?
Imagine that we count on infinite computation power, an infinite amount of data and we have an infinite amount of time to wait for a model to learn. In such a scenario, we want to have some data ...
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Which neural network is better?
MNIST dataset with 60 000 training samples and 10 000 test samples.
Neural network #1. Accuracy on the training set: 99.53%. Accuracy on the test set: 99.31%.
Neural network #2. Accuracy on the ...
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Why do the performance of DL models increase with the volume of data while that of ML models will flat out or even decrease?
I have read some articles and realized that many of them cited, for example, DLis better for large amount of data than ML.
Typically:
The performance of machine learning algorithms decreases as the ...
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Given M binary variables and R samples, what is the maximum number of leaves in a decision tree?
Given M binary variables and R samples, what is the maximum number of leaves in a decision tree?
My first assumption was that the worst case would be a leaf for each sample, thus R leaves maximum. Am ...
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How to input a list into my model and not have it care about order
I'm trying to predict a list of numbers, e.g:
[23,55,198,200,64]
The data I have includes multiple things, along with:
The numbers from the previous run (These ...
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Use of multiple models vs training a single model for multiple outputs
So let's say I have data with numerical variables A, B and C.
I believe that the value of <...
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What is Inductive bias?
Bias in a neural network is an additional neuron to be fired i.e let $y=a+bx$ where $a$ is a bias term.
Do we have any difference between bias and inductive bias?
How Inductive bias is helpful in ...
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Multiple solutions with same minima in MLP with same weights
I came across an excercise on deep learning from here.
It goes as follows:
Consider a simple MLP with a single hidden layer of $d$ dimensions in the hidden layer and a single output. Show that for any ...
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Theoretical basis for neural network "effort"
I might be in danger of having my question closed as "not clear what I'm asking for," but here goes.
Suppose we have a simple feedforward network. It has a few layers, each layer has a "...
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What is the opposite of baseline?
I have created a prediction model and on the one hand I have to compare it with other baseline models, and on the other hand, I have to compare it with the ideal approach (supported by additional data)...
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Are non-relu activations better for small/ dense datasets?
Building on the questions below, the only conclusion I could draw from the answers was that ReLu is less computationally expensive and better at sparsity.
Why is ...
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Unbiased Predictions for all Distinct Training Subsets
Suppose I have a data set $\left(X_i \in \chi, y_i \in \zeta \right)$ where $X_i$ and $y_i$ correspond to instances and labels, and $\chi$ and $\zeta$ correspond to the space where $X_i$ and $y_i$ ...
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How to get the maximum likelihood estimate of the categorical distribution parameters using Lagrange optimization?
Let's say our data is discrete-valued and belongs to one of $K$ classes.
The underlying probability distribution is assumed to be a categorical/multinoulli distribution given as $p(\textbf{x}) = \...
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Why does Jim Gray call "data-driven science" a new paradigm?
Wikipedia it says about data science:
Turing award winner Jim Gray imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational and now data-driven) and ...
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Can a neural network be of variable depth?
It is very common for neural networks to be asymmetric about the x axis, that is, to have many more nuerons in the first few layers than in the last few layers. Common example:
But can neural ...
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What is an object detection problem with only one class called?
Object detection is defined as the problem in which a model needs to figure out the bounding boxes and the class for each object. A lot of ML solutions for object detection base around having "two ...
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Gradient for starting Backpropagation
I was reading this nice tutorial about Pytorch's basics:
https://pytorch.org/tutorials/beginner/pytorch_with_examples.html
In the first example (pure Numpy), the author starts the backward phase by ...
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What is the best approach for send time optimization? [closed]
I could no find a lot information about how the companies doabout send time optimization, either for push notifications or email campaigs. having historical data about clicks and sends what would be ...
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Is the search space of Hyperparameters Continuous or Discrete?
I am looking into hyper-parameter tunning and was curious about whether the search space is considered continuous or discrete?
My understanding of both those cases:
1. Continuous would make it 'easier'...
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What is the name of this statistical interaction?
What is the name of the following statistical / informational interaction:
given A, I know exactly what B is.
given B, I know to some extent what A is.
I'm not looking for a probability but rather ...
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Structuring experiment/training data with months in mind
We're using a whole year's data to predict a certain target variable.The model works like data - OneHot encoding the categorical variables - MinMaxScaler - PCA (to choose a subset of 2000 components ...
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Encoding correlation
I have rather theory-based question as I'm not that experienced in encoders, embeddings etc. Scientifically I'm mostly oriented around novel evolutionary model-based methods.
Let's assume we have ...
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Explanation of inductive bias of Candidate Elimination Algorithm
The definition of inductive bias says that
The inductive bias (also known as learning bias) of a learning algorithm is the set of assumptions that the learner uses to predict outputs given inputs ...
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Understanding LSTM Keras implementation
So I understand what LSTM units are. But I have trouble understanding the implementation / function in Keras framework.
Let's say, I add a layer
...
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Popular classification algorithms over time
In the book "Deep Learning with Python" by Francois Chollet (2018), in section 1.2.4 one can find:
Decisions trees learned from data began to receive significant research interest in the ...
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Is it possible for a neural net to score as high as a different form of supervised learning?
I've been working with the Adult Census Income dataset from UCI
http://archive.ics.uci.edu/ml/datasets/adult
I've created two different models, one using a gradient boosted classifier with sklearn, ...
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A trick used in Rademacher complexity related Theorem
I am currently working on the proof of Theorem 3.1 in the book "Foundations of Machine Learning" (page 35, First edition), and there is a key trick used in the proof (equation 3.10 and 3.11):
$$\...
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An ambiguity in SVM equations about misclassified data
I have encountered an ambiguity in SVM equations.
As is stated in Chris Bishop's machine learning book, the optimization goal in SVM is to maximize this function:
$$C\sum\limits_{n = 1}^N {{\xi _n}} ...
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A question on realizable sample complexity
I came across the following exercise, and I just can't seem to crack it:
Let $l$ be some loss function such that $l \leq 1$. Let $H$ be some hypothesis class, and let $A$ be a learning algorithm. ...
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What could cause validation set to consistently perform better than training?
I'm training a neural network with a very small dataset just to get things set up, before training on a much larger set. (I only have about 500 data points available to me at this time, with more ...
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Decentralized machine learning
Currently, to train a model, you need to collect a huge blob of data.
Are there feasible concepts of decentralized machine learning? Like, feed the model somehow from isolated data sources, or merge ...
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What does it mean when someone says "Most of the data science algorithms are optimization problems"
I was trying to understand the Gradient Descent algorithm from this article and the author says
Most of the data science algorithms are optimization problems
I come from software engineering ...
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What should be the requirement for training data in order to obtain a good regression model using neural network?
I have made a neural network regression model using the theory for the first time and would like to clarify some basic doubts, whose concrete answers I couldn't find yet.
Data:- I have 3000 samples ...
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Intuition behind Occam's Learner Algorithm using VC-Dimension
So I'm learning about Occam's Learning algorithm and PAC-Learning where for a given hypothesis space $H$, if we want to have a model/hypothesis $h$ that has an True error of $error_D \leq \epsilon$, ...
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Is the hypothesis space spanned by kernel evaluations on datapoints equivalent to the hypothesis space of linear functionals in the feature space?
when studying kernel methods a few years ago I got a bit confused with the concepts of feature space, hypothesis space and reproducing kernel Hilbert space. Recently, I thought a little about ...
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Are all classifiers linear in some high dimensional space?
Of all possible classifiers (including SVMs, locally weighted regression, softmax regression, lots others I'm sure I don't know about, etc.), are they all linear in some high dimensional space?
E.g. ...
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How do you calculate the information capacity of a neural network?
Let's say I wanted to train a neural network to teach it the rules in a decision tree, so I generated a dataset by feeding arrays of random numbers into the pre-trained decision tree, and then used ...
