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Questions tagged [features]

For questions related to features in the context of machine learning and, in general, AI.

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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 ...
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Neural Network with numerical data and sentences as features

I'm beginning in the words A.I. features. My current problem is that I want to create Neural Network that takes as input numerical data and also words as data (by words, I mean multiple sentences) to ...
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1 vote
1 answer
32 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 ...
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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 ...
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1 vote
1 answer
41 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 ...
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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: ...
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What can be the reasons for validation MSE < training MSE at beginning of training and network failing to generalize afterwards?

I am using a Convolutional Neural Network for regressing time series data. The objective is to predict an obfuscated metric. The training metrics and losses are as follows. The val_loss is lower than ...
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1 vote
0 answers
80 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 ...
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1 vote
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28 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 ...
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28 views

What is the meaning of "The linear model can now describe the function as increasing in $h_1$ and decreasing in $h_2$"?

In the famous Deep Learning book by Goodfellow et al., it is mentioned on page 169 in the caption of Figure 6.1 that The linear model can now describe the function as increasing in $h_1$ and ...
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6 votes
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83 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 ...
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1 answer
46 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 ...
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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 ...
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2 votes
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33 views

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 ...
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1 answer
55 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 ...
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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 ...
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1 answer
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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, ...
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1 vote
1 answer
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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 ...
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Increased performance using monotonic constraints with neural networks

I see that with the xgboost library, we can tell the training process that some features are necessarily monotonic with the model's output - https://xgboost.readthedocs.io/en/latest/tutorials/...
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1 vote
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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 ...
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4 votes
2 answers
202 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 ...
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3 votes
2 answers
87 views

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 ...
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1 vote
1 answer
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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 ...
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2 votes
1 answer
53 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 ...
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1 vote
1 answer
77 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 ...
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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,...
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1 vote
2 answers
579 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 ...
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0 votes
1 answer
104 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 ...
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1 vote
1 answer
38 views

When is adding a feature useless?

I'm building a model, where, from a feature set A, I want to predict a target set C. I need to understand if another feature set B, together with A, can improve my model performances, instead of using ...
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0 votes
2 answers
39 views

Feature scaling strategy for many feature with very large variation between them?

I was running into a situation in which my input feature experience a very large variation in term of magnitude. Particularly, consider feature 1 belong to group 1 and feature 2 3 4 belong to group 2, ...
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2 votes
1 answer
3k views

What are bag-of-features in computer vision?

In computer vision, what are bag-of-features (also known as bag-of-visual-words)? How do they work? What can they be used for? How are they related to the bag-of-words model in NLP?
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2 votes
2 answers
81 views

Can we train the model to detect real users with only positive labels?

We have hundreds of thousands of customers records, and we need to take the benefits of our data to train a model that will recognize fake entries or unrealistic ones for our platform, where customers ...
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3 votes
1 answer
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How are small scale features represented in an Inverse Graphics Network (autoencoder)?

This post refers to Fig. 1 of a paper by Microsoft on their Deep Convolutional Inverse Graphics Network: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/11/kwkt_nips2015.pdf Having ...
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4 votes
0 answers
61 views

Training and inference for highly-context-sensitive information

What is the best way to train / do inference when the context matters highly as to what the inferred result should be? For example in the image below all people are standing upright, but because of ...
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5 votes
1 answer
733 views

What is "conditioning" on a feature?

On page 98 of Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning the author writes; Redacted phase space: Studying the distribution of inputs ...
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3 votes
1 answer
45 views

Do the eigenvectors represent the original features?

I've got a test dataset with 4 features and the PCA produces a set of 4 eigenvectors, e.g., ...
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0 votes
1 answer
54 views

What is the correct name for state explosion from sensor discretization?

The position of a robot on a map contains of an x/y value, for example $position(x=100.23,y=400.78)$. The internal representation of the variable is a 32bit float which is equal to 4 byte in the RAM ...
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1 vote
0 answers
70 views

How can I use gradient boosting with multiple features?

I'm trying to use gradient boosting and I'm using sklearn's GradientBoostingClassifier class. My problem is that I'm having a data frame with 5 columns and I want ...
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1 vote
1 answer
59 views

When doing binary classification with neural networks, how can I order the importance of the features for a class?

I have a simple neural network for binary classification. The input features include age, sex, economic situation, illness, disability, etc. The output is simply 1 and 0. I would like to order the ...
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2 votes
0 answers
56 views

Does coarse coding with radial basis function generate fewer features?

I am learning about discretization of the state space when applying reinforcement learning to continuous state space. In this video the instructor, at 2:02, the instructor says that one benefit of ...
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1 vote
1 answer
58 views

If I wanted to calculate multiple feature maps in a convolutional layer, should the filters be trained individually?

Assume I have an input of size $32 \times 32 \times 3$ and pass it to a convolution layer. Now, if my kernel size were to be $5 \times 5 \times 3$ and the depth of my convolution layer were to be 1, ...
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2 votes
2 answers
155 views

Is the number of feature maps equal to the number of kernels in the LeNet 5 architecture?

In LeNet 5's first layer, the number of feature maps is equal to the number of kernels. However, the second convolutional layer has a depth different from the 3rd layer. Does the filter size dictate ...
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1 vote
1 answer
442 views

How can $\nabla \hat{v}\left(S_{t}, \mathbf{w}_{t}\right)$ be 1 for $S_{t}$ 's group's component and 0 for the other components?

In Sutton's RL:An introduction 2nd edition it says the following(page 203): State aggregation is a simple form of generalizing function approximation in which states are grouped together, with one ...
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1 vote
1 answer
3k views

What is a temporal feature?

What is a temporal feature, what features make something temporal in nature? Is this problem agnostic? How does it change from different fields of study?
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2 votes
2 answers
117 views

How do neural networks weigh multiple inputs/features of different dimensionality?

I am confused about how neural networks weigh different features or inputs. Consider this example. I have 3 features/inputs: an image, a dollar amount, and a rating. However, since one feature is an ...
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1 vote
2 answers
170 views

Which neuron represents which part of the input?

In a neural network, each neuron represents some part of the input. For example, in the case of a MNIST digit, consider the stem of the number 9. Each neuron in the NN represents some part of this ...
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3 votes
1 answer
282 views

Does the correlation between inputs affect the model performance?

I'm currently working on a regression problem and I have 10 inputs/attributes. What should I do if there are correlations between different features of the input data? Does the correlation between ...
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3 votes
1 answer
67 views

When working with time-series data, is it wrong to use different time-steps for the features and target?

When working with time-series data, is it wrong to use daily prices as features and the price after 3 days as the target? Or should I use the next-day price as a target, and, after training, predict 3 ...
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4 votes
1 answer
1k views

How to handle varying types and length of inputs in a feedforward neural network?

After learning the basics of neural networks and coding one working with the MNIST dataset, I wanted to go to the next step by making one which is able to play a game. I wanted to make it work on a ...
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6 votes
1 answer
261 views

How to add more features to the input of a machine learning algorithm?

I am trying to perform a binary classification of tweets using machine learning. The usual way of doing this seems to be putting a hand-classified tweet's words into a big vector, then use that ...
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