All Questions
73 questions
1
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77
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Regression model is doing exceptionally very well on time series
I have the following task to do: I have time series data. Training by the consecutive 3 days to predict the each 4th day. Each day data represents one CSV file which has dimension 24x25. Every ...
- 113
0
votes
2
answers
26
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What the difference between influence and dependence?
I'll give an example on height and weight. Weight and height are correlated, but it's not necessarily the case that a tall person weighs more or that someone who weighs more is a tall person - and ...
- 23
2
votes
2
answers
55
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What is the sensitivity of coefficients in multicollinearity?
I've heard that the coefficients in multicollinearity are very sensitive, and can change due to small changes in the data.... Isn't it a problem with the dataset itself that we have different data? ...
- 23
0
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0
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19
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I developed a fairly simple custom feature selection method for a problem I had. Does it already exist?
I had a specific problem where I had a leave-one-subject-out cross-validation scheme that was a little complex in terms of scoring.
Specifically, I had 21 subjects, and for each subject I had between ...
0
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0
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11
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How do I represent the relationship between time and brightness in a machine learning model?
I have done all the feature engineering and am ready to start making a machine learning model that predicts the type of variable star based on its light curve. I have broken down this light curve into ...
- 101
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0
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21
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Deep Learning: Architecture vs. Features for Performance?
In deep learning, when aiming for peak metric performance, is a well-designed architecture with imperfect features/dataset generally preferable to a poorly designed architecture with high-quality ...
0
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0
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45
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What does a feature's integrated gradient actually represent in the context of a regression task?
I've been reading about IGs, but all the articles I've read describe it in terms of a classification task. And in that context it makes a little more sense to me as the change in probability for a ...
0
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0
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10
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How do nonlinear relationships affect casuality determination
Let's assume that I have only one independent variable and one dependent, and
I have a great model with minimal error which deals well with predicting.
Let's also assume that I do no know the true ...
- 303
0
votes
1
answer
50
views
Is a linear regression model able to figure out the relation of division among two features?
I have a dataset that consists of data about students. The features are things such as passed credits, failed credits, ...
- 103
1
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0
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73
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Compare two songs content using Audio Spectogram Transformer
I'm trying to establish a similarity metric between two songs. To do this I'm using the AST model on HuggingFace. This model basically works in a way very similar to a ViT but applied to spectograms ...
- 111
0
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1
answer
50
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Machine Learning Algorithm for identifying the factors contributing to academic performance of students
I have a dataset with several qualitative and quantitative attributes, including age, location (longitude, latitude), city, parent occupation, family size, GPA etc. My task is to find the attributes/...
- 43
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0
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20
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Predict outputs based on a variable subset of inputs
To simplify this: I have 5 columns in my dataset -> A, B, C, D and E. I want the neural network to predict the rest of the outputs based on a subset of inputs.
For example:
Case #1
Inputs -> (A) ...
- 1
0
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0
answers
11
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Finding invariant feature areas within representation vector for each meta-class/group?
I have pairs of images which are not the same class, but are from the same meta-class/group. I have a standard CNN which produces a representation for each sample.
If I have several pairs of images ...
- 11
1
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0
answers
87
views
What is the best way to train a neural network with a variable number of inputs?
Suppose I have a neural network with 5 inputs: [A,B,C,D,E]
There is only 1 output. The expected accuracy of the model should increase when all 5 inputs are ...
- 11
0
votes
2
answers
43
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Non constant Feature Importance
I have a financial dataset which has 10 years worth of data. The aim is to build a regressor capable of predicting next year sales. So, if I want to predict sales for 2024, I could use data from 2023, ...
- 1
0
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0
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39
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Calculating class-specific permutation feature importances for multilabel classification problem
I would like to apply the permutation feature importance technique to rank the features of a siamese network model that I trained. I am currently using this siamese network to perform some kind of ...
1
vote
1
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172
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Is there any advantage to providing multi-dimensional input to torch modules?
Most layer types in torch.nn such as torch.nn.Linear accept input with more than one dimension. Is there any advantage in doing so if you can shape your data to represent a certain arrangement in ...
- 13
1
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1
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126
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Feature vector representation of probability distribution
I have a series of multiple probability distribution like this:
[
[0.2, 0.3, 0.5],
[0.1, 0.2, 0.7],
...
]
Do you have any suggestions how I can represent this ...
- 43
1
vote
1
answer
90
views
Transfer Learning for Solar Energy Production Forecasting with LSTM: Generalized vs. Specialized Models
I am working on a solar energy production forecasting problem using LSTM multi-step models to predict 1/4/8h ahead of solar energy production for different solar installations. Our goal is to help ...
1
vote
1
answer
77
views
Features for a Content-Based recommendation system
I'm working on a hybrid recommendation system (collaborative and content-based) for an online ordering/shopping app. So far I've managed to identify a data-source for the collaborative model (likely ...
- 11
1
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0
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119
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Understanding the features given in Example 13.1 of Sutton and Barto
I'm struggling to understand the notation used to represent the features within Example 13.1 (Short corridor with switched actions" in the Sutton and Barto RL book.
I assume as it is a free pdf ...
- 111
1
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0
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41
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Why "Good Model" that performs great on holdout validation data fails on production data
I have this binary regression model that has ~500 futures with an unbalanced dataset with the following results.
...
- 23
1
vote
1
answer
313
views
How to handle out-of-bound values in Production data?
So I have this model but the data may vary. And it is virtually impossible to always have the values in bounds. If I do I`d have to use larger period leading to concept shift which is worse.
The ...
- 23
2
votes
1
answer
695
views
How to calculate a meaningful distance between multidimensional tensors
TLDR: given two tensors $t_1$ and $t_2$, both with shape $(c,h,w),$ how shall the distance between them be measured?
More Info: I'm working on a project in which I'm trying to distinguish between an ...
- 371
4
votes
2
answers
60
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Is there any proper literature on the types of features that different layers of a deep neural network learn?
Let's consider a deep convolutional network. It seems that there is some consensus on the following notions:
1. Shallow layers tend to recognise more low-level features such as edges and curves.
2. ...
- 141
2
votes
2
answers
411
views
How to convert color information to 1D feature vector?
We are making a classification model that takes a clip of a movie as an input and predicts who the director is. Roughly speaking, it will be a model that understands film directors' unique style.
We ...
2
votes
2
answers
5k
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What is the difference between features and inputs in machine learning?
I have seen many places that features and inputs have been used interchangeably when talking about machine learning especially deep neural networks. I want to know if they are indeed the same thing or ...
- 145
1
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1
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747
views
What kind of features does each node have as an input graph to a graph neural network?
What kind of features does each node have as an input graph to a graph neural network? For example, we want to do image classification with GNN, what are the features of each pixel? Or if anyone could ...
- 11
1
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1
answer
60
views
What is the name of a feature space which has consistant distance-related properties?
What is the word describing a feature space where distance between two elements has a decisive informational value, whatever the pair of elements is?
For example if a model creates embeddings for ...
- 11
1
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1
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421
views
When is it appropriate to use information like sex or race in ethical machine learning?
I'm a little confused on best practices regarding ethical ML. Specifically, I've seen in some courses that when building a model that affects people, it's helpful to have sensitive personal ...
- 113
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0
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51
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Does it make sense to compare images (samples) with words (features)?
Consider the following paragraphs from the introduction of the chapter named Recurrent Neural Networks from the textbook titled Dive into Deep Learning
So far we encountered two types of data: ...
- 3,990
1
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0
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91
views
What do state features mean in the context of inverse RL?
I am reading Zeibart's Inverse RL paper, and it states -
The agent is assumed to be attempting to optimize some function that linearly maps the features of each state, $f_{sj} \in \mathbb{R}^k$, to a ...
- 670
1
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0
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34
views
What is the difference between the $Q_a$ calculated to update delta and those to select next action in the exploitation phase?
As the title suggests, I have a doubt about the computation of the $Q_a$ used to update the delta and the $Q_a$ used to select the next action in the exploitation phase, as shown below (source of ...
7
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2
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455
views
How can a neural network distinguish a rotated 6 and 9 digits?
Rotated MNIST is a popular dataset for benchmarking models equivariant to rotations on $\mathbb{R}^2$, described by $SO(2)$ group or its discrete subgroups like $\mathbb{Z}^{n}$:
Group equivariant ...
- 2,754
0
votes
1
answer
1k
views
When can we call a feature "hierarchical"?
Features in machine learning are the attributes of the elements of a data set. They are considered as random variables in probability.
Consider the following excerpt from 1.1: The deep learning ...
- 3,990
2
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1
answer
439
views
Is it true that channels always represent colours of an image?
Convolutional neural networks are widely used in image-related tasks in artificial intelligence.
The input of a conventional neural network is generally an image. The output of a convolutional neural ...
- 3,990
2
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1
answer
2k
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What are examples of node 'features' in graph networks?
Context: I was reading Chapter 3 in the following book (here) about graph representation learning. Before I get to node embeddings, I wanted to make sure that I do understand what is meant by the ...
- 165
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1
answer
1k
views
How to find "relationships" between two data representations?
I am a researcher in a field, and new to the whole of AI and machine learning techniques. May the following question is trivial or not framed in the ML language but I try my best.
I have two sets of ...
- 15
2
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0
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936
views
What does it mean by "low-level" and "high-level" in features generated by CNN?
Across the literature, the terms "high-level" and "low-level" are generally used as an adjective to the features generated by the convolution neural network as intermediate ...
- 3,990
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votes
1
answer
90
views
Why disentangling the features of variation in representation?
Consider the following excerpt from abstract of the research paper titled Better Mixing via Deep Representations by Yoshua Bengio et al.
It has been hypothesized, ...
- 3,990
1
vote
1
answer
439
views
Can I always interpret features as random variables in machine learning safely?
Consider the following statements from Chapter 5: Machine Learning Basics from the book titled Deep Learning (by Aaron Courville et al.)
Machine learning tasks are usually described in terms of how ...
- 3,990
1
vote
0
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311
views
How to train a machine learning model with multiple attributes and one target value?
I'm working on a machine learning problem where I need to guess which customers will churn and which of them will continue to be customers.
I have $X_0, X_1, X_2, X_3, X_4, X_5$ and $X_6$ attributes ...
4
votes
3
answers
1k
views
When is it necessary to manually extract features to feed into the neural network rather than providing raw data?
Usually, Neural Networks uses raw data. You do not need to extract features manually. NN's can find & extract good features which is a pattern of an image, signal or any kind of data. When we ...
- 257
3
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2
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122
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How do we know that the neurons of an artificial neural network start by learning small features?
I'd like to ask you how do we know that neural networks start by learning small, basic features or "parts" of the data and then use them to build up more complex features as we go through ...
- 573
1
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1
answer
53
views
Does feature scaling have any benefits if all features are on the same scale?
By scaling features, we can prevent one feature from dominating the decisions of a model. For example, say heights (cm), and age (years) are two features in my data. Since range of heights is larger ...
- 1,040
2
votes
1
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69
views
Does the weight vector form imply feature space curvature?
I came across this sentence when exploring a simple nearest neighbor classifier method using Euclidean distance (link):
The slightly odd thing about using the Euclidean distance to compare features ...
- 21
1
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1
answer
96
views
How to predict the best from a set of messages - best practice
Suppose I have a set of messages A,B,C,D and I want to produce the best message for a website user at a given time.
For training I plan to show random users a random single message [A/B/C/D] and fill ...
- 11
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1
answer
77
views
Is it possible to flip the features and labels after training a model?
The goal of this program is to predict a game outcome given a game-reference-id, which is a serial number like so:
id,totalGreen,totalBlue,totalRed,totalYellow,...
- 11
1
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2
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3k
views
What does the depth of a decision tree depend on?
In these notes, we have the following statement
The depth of a learned decision tree can be larger than the number of training examples used to create the tree
This statement is false, according to ...
- 13
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1
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687
views
What is the impact of the number of features on the prediction power of a neural network?
What is the impact of the number of features on the prediction power of an ANN model (in general)? Does an increase in the number of features mean a more powerful prediction model (for approximation ...
