Questions tagged [terminology]

Indicates questions asking about the use and meaning of specific technical words/concepts in statistics.

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Terminology: what is the correct word for the quantity of “association” between x value and classes

I am looking for a word for the quantity of "association" between a variable x value and a classification class. For example, let's imagine we have two two classes $A$ and $B$ and two inputs ...
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35 views

I need help to write an essay about: probabilistic method || Occam's razor || mathematics in the 21st

I am interested in one of the master's programs in Data Science. In the application process I need to submit an essay of 1,000 words about one of the following topics: Drawbacks of the probabilistic ...
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12 views

What is the correct name for a stacked bar chart with a single bar (series), and many stacked segments?

I had a treemap that was better expressed in a linear format. This resulted in a chart that I don't know the name of. It's something like a stacked bar chart, with just a single column/row: (This ...
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1answer
21 views

Technical term to describe/differentiate the structure of an instance by the number of samples per instance ('tuple' vs 'time series')

Several terms are used to describe the structure of instances of a dataset. For example, an instance can be 'univariate' if it depends on only one variable or 'multivariate' if it depends on 2 or more ...
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History that lead to the word “predict” being used for the application of a model on data

Background The framework scikit-learn uses "predict" for the application of model on (new) input data and I have seen many people use that term. In the scientific papers that I have read (...
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1answer
105 views

Terminology to distinguish between ML methods and optimization methods (PSO, ACO..)

I am currently writing a scientific thesis which consists of two parts. In the first part I am building ML models with neural networks, support vectors etc. and the second part is about finding global ...
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17 views

What is the correct terminology for neural network architectures that expand in their internal layers? (The opposite of a “bottleneck”)

I am aware that an auto-encoder contracts in its hidden layers to form a bottleneck. In contrast to this, is there a good name for the kind of cell, block or architecture that expands in its internal ...
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1answer
24 views

Intuitive explanation of Adversarial machine learning

How would you explain Adversarial machine learning in simple layman terms for a non-STEM person? What are the main ideas behind Adversarial machine learning?
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1answer
22 views

The upper range of a collected dataset is most likely accurate, but the rest may suffer biased omissions: How to call this phenomenon?

Background: In collecting a dataset of a specific unit ordered by a numeric variable, it is possible that the upper 'cloud' of the dataset is correct, while the 'tail' seems inaccurate. I can thus ...
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17 views

Machine learning terminology (method, model, architecture, task, objective)

I got a bit confused about the usage of machine learning terminology in books / papers / discussions that seems somehow not completely consistent to me. Therefore, I want to know if you would agree ...
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1answer
33 views

Can neural networks be considered a method for data analysis?

I am in the process of writing my thesis. In this thesis I am stating that neural networks can be considered a method for data analysis. After a bit of though I am now unsure if this claim is ...
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284 views

Who invented the concept of over-fitting?

I list the references that I found so far. Shortly, the first appearance of the term was in 1670, first appearance in in close meaning was in 1827, first appearance in a biological paper was in 1923 ...
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52 views

Which term is correct Datafication or Datification?

I have recently started reading Introduction to Data Science: A python approach to Concepts, Techniques, and Applications and taking notes on Data Science. Chapter 1 repeatedly uses the term ...
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2answers
230 views

Loss function in GradientBoostingRegressor

I was looking at the Scikit-Learn documentation for GradientBoostingRegressor. Here it says that we can use 'ls' as a loss function which is least squares regression. But i am confused since least ...
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1answer
39 views

Terminology in machine learning: exogenous features vs external features

I am currently writing a scientific paper and do not know whether to call some of my input features of my neural network either external or exogenous. My neural network receives as input features like ...
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27 views

What is the difference between “Document Layout Analysis” / “Document Understanding” / “Document Structure Analysis”

All three terms sound super similar: [...] document layout analysis is the process of identifying and categorizing the regions of interest in the scanned image of a text document. A reading system ...
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1answer
44 views

What's the of all values above some percentile called? How do I get it in pandas?

Let's say I have a list of numbers, and I want the mean of all numbers that are greater than the 95th percentile. Is there some standard term for that value? ("Mean of a histogram bin"? &...
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1answer
23 views

How to distinguish between different values of a hyperparameter in communication?

From Wikipedia: In machine learning, a hyperparameter is a parameter whose value is used to control the learning process. If we go by the definition of parameter in What's the difference between an ...
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2answers
342 views

What is the difference between a “cell” and a “layer” within neural networks?

So I understand what "layers" are. If you have 5 layers in your model, your data basically gets transformed 5 times via 5 activation functions. The number of "neurons" within a ...
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24 views

Is there any research on zonal OCR / field level ORC / template OCR?

I've recently found the term "Zonal OCR": (source 1, 2, 3, 4). It seems to be essentially OCR, but restricted to relevant parts of the document. The interesting task about which I want to ...
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1answer
24 views

Terminology Question: Place Coding

While reading the paper "Backpropagation Applied to Handwritten Zip Code Recognition", one of the original papers attributed to the development of convolutional neural networks, on page 3, ...
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1answer
16 views

What is the appropriate approach for training time series data against multiple, consecutive labels?

Let's say we have a time series $\{{\bf x}_i\}$ of features and are trying to learn to predict a time series $\{t_i\}$ using a neural network. Our goal is to be able to predict the time series value $...
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5answers
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What is the difference between explainable and interpretable machine learning?

O’Rourke says that explainable ML uses a black box model and explains it afterwards, whereas interpretable ML uses models that are no black boxes. Christoph Molnar says interpretable ML refers to the ...
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1answer
485 views

How are the channels handled in CNN? Is it independently processed or fused?

Let's assume that we are talking about 2D convolutions applied on images. In a grayscale image, the data is a matrix of dimensions $w \times h$, where $w$ is the width of the image and $h$ is its ...
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1answer
89 views

What is the layer above/below in a NN?

In the lecture notes of CS231n, it says (emphasis mine) ... There are three major sources of memory to keep track of: From the intermediate volume sizes: These are the raw number of ...
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1answer
96 views

Terminology - regression with one output and multiple output variables

I am trying to predict the response when the input is represented by Fourier transform. These form the features and are typically represented as a vector, $x_1,x_2,...,x_d$ where $d$ is the length of ...
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1answer
2k views

Ordered and unordered categorical features – terminology

In the famous book "The Elements of Statistical Learning" by Hastie et al., the authors denoted unordered categorical variables as qualitative variables / nominal variables / factors. I wonder, do ...
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1answer
424 views

Regression: What defines Linear and non-linear models or functions

Linear regression is used when there is a linear relationship between the input and output variables. Does this linear relationship mean that there is no power over the variables or the parameters? In ...
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3answers
105 views

About different structures of neural network

https://www.mathworks.com/help/deeplearning/ref/fitnet.html is the tutorial that I am following to understand fitting data to a function. I have few doubts regarding structure and terminologies which ...
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1answer
149 views

Conceptual questions on MLP and Perceptrons

I am facing some confusion regarding the terminologies assocaiated to classification and regression problems esp. using the MLP and Perceptron models. These are the following: 1) When the data is ...
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1answer
46 views

What are the points called in a dataset where the points have the same features but different labels

What are the points called in a dataset where the points have the same features but different labels? For example, I am trying to predict whether some object is a hot dog. But I only have access to ...
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1answer
2k views

Could anyone explain these terms, “input space”, “feature space”, “sample space”, “hypothesis space”, “parameter space” with a concrete example?

People use these terms "input space", "feature space", "sample space", "hypothesis space", "parameter space" in machine learning. Could anyone explain these terms with a concrete example, such as ...
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2answers
41 views

Is the _error_ in the context of ML always just the difference of predictions and targets?

Simple definitional question: In the context of machine learning, is the error of a model always the difference of predictions $f(x) = \hat{y}$ and targets $y$? Or are there also other definitions of ...
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1answer
40 views

What is the level of measurement / name of the scale of list-features?

If you look at publications, you can have a dataset title of publication list of authors number of pages year of publication The Level of measurement of "number of pages" is interval scale, the ...
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2answers
41 views

What is the standard terminology for the output matrix of a hidden layer in a CNN?

The output of a hidden layer $h_i$ in a convolutional neural network is (generally) a 3D grid of values. These values are the outputs of the neurons of layer $h_i$. Is there a standard way to refer to ...
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2answers
62 views

What's the name of data points that trained model gets on input in production?

I need some machine learning vocabulary advice. What do you call input data that a trained model gets in production? I know that labelled or unlabelled data it gets during training is called ...
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1answer
2k views

Terminology: “flat geometry” in the context of clustering

Sklearn's documentation refers to "flat" or "non-flat" geometry of clusters to describe the use-cases of their implemented clustering algorithms. Those terms are not directly defined. However, the ...
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2answers
413 views

What fits in a Data Description Report/ Data Exploration Report?

So I am trying to get familiar with Crisp-DM and found the terms "Data Description Report" and "Data Exploration Report", which seem oddly vague in their definition. So far I only found this right ...
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3answers
2k views

Is a “curve” considered “linear”?

In linear regression, we are fitting a polynomial to a set of data points. In Bishop's book of Pattern Recognition & Machine Learning, there are a few examples where the fit is a curve or a ...
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1answer
35 views

If this is “big-endian”, then what is this? [closed]

If an ISO 8601 date is “big-endian”, i.e. the most significant bit is first and the least significant is last, then what do we call data structures where the most significant part is first/at the top, ...
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2answers
468 views

Terminology - cross-validation, testing and validation set for classification task

Confusion1) If k=10 then does this mean that 90% is for training and 10% for testing? So always we have k% for testing? ...
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1answer
65 views

Collection of several learners [closed]

I have few questions for which I could not extract answers from text books and online tutorials. Therefore, will be extremely grateful if the following points are clarified. 1) If I want to apply SVM,...
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434 views

What are towers in inception architecture and tensorflow?

My understanding of towers in inception architecture and in tensorflow terminology is that they are part of a neural network model for which separate computation can happen on forward phase and ...
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3answers
43k views

What does “baseline” mean in the context of machine learning?

What does "baseline" mean in the context of machine learning and data science? Someone wrote me: Hint: An appropriate baseline will give an RMSE of approximately 200. I don't get this. Does he ...
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2answers
3k views

What is the difference between bootstrapping and sampling in reinforcement learning?

I have come across a David Silver's slide which contains both the terms "bootstrapping" and "sampling". Is there any realistic example which helps me to understand the concepts better.
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1answer
67 views

What is an aspect in opinion mining?

It's quite a challenging task of aspect extraction in the field of opinion mining if you look at the number of related papers. But what is an aspect in the field of opinion mining?
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Term for Methods of Representing Repeated Text in Classifier

A colleague told me that there are terms for two different methods of representing repeated text in the training set for a classifier, but he could not recall them. What are the terms for the options ...
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1answer
8k views

What is the difference between “expected return” and “expected reward” in the context of RL?

The value of a state $s$ under a certain policy $\pi$, $V^\pi(s)$, is defined as the "expected return" starting from state $s$. More precisely, it is defined as $$ V^\pi(s) = \mathbb{E}\left(R_t \mid ...
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3answers
7k views

What is the difference between outlier detection and anomaly detection?

I would like to know the difference in terms of applications (e.g. which one is credit card fraud detection?) and in terms of used techniques. Example papers which define the task would be welcome.
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2answers
5k views

What is representation learning?

I am reading the Chapter-1 of the Deep Learning book, where the following appears: A wheel has a geometric shape, but its image may be complicated by shadows falling on the wheel, the sun glaring ...