Questions tagged [terminology]
Indicates questions asking about the use and meaning of specific technical words/concepts in statistics.
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Confusion over training accuracy vs. training loss
I had a small discussion with my friends on overfitting and we became confused over the two terms: "training accuracy" and "training loss (or cost)". This is the first time I've ...
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Are "textbook backpropagation" still relevant?
The above backpropagation algorithm is taken from Shalev Shwartz and Ben-David's textbook: Understanding Machine Learning. This algorithm is described in the same way as the one in Mostafa's textbook, ...
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SVM: difference between soft and kernel technique
Most articles and textbooks say that soft margin SVM is used is the data is messy/not linearly separable. We introduce slack variables to make the data linearly separable. Kernels are used when the ...
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Formal terminology: metafeatures then groupby (GROUP BY) operation
In ML training and other analytics I often combine features to produce a 'metafeature' and then perform a 'groupby' (pandas) or 'GROUP BY' (SQL) query. What is the technical term for this operation?
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building embeddings for Phrases from scratch
I have a datadet with many phrases which I would like to embed them from scratch. I dont want the cosine of the words in order to get a phrase embedding, this is because the phrases may appear in a ...
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How does an LLM "parameter" relate to a "weight" in a neural network?
I keep reading about how the latest and greatest LLMs have billions of parameters. As someone who is more familiar with standard neural nets but is trying to better understand LLMs, I'm curious if a ...
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Blocks, factors and treatments in designed experiments
I have recently began studying a course on Designed Experiments and am having some trouble understanding some of the terminology. I've looked at some other answers on the site and I think that I am ...
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Looking for a standardized API for terminology services
I'm looking for a standard to access terminology services. A terminology service provides an generalized API to multiple terminologies like ICD10, MedDRA, Snomed, Loinc etc. But I'm looking for a ...
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What would be the best word/way to describe the differences in two deep learning models (deep, wide, shallow, big or small model?)
I did experiments using a backbone deep network. I took it as it is and then I decreased the number of filters and did some other experiments. Note that, I did not change the number of layers of the ...
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Problem with data representing classes that weren't present during supervised training
During prediction phase, fully trained supervised models may have to deal with data representing new classes, that weren't part of the training and test sets. A real world example for this issue is ...
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variance explained by model
This is a question for beginners.
edited 19/11.
I am really confused by the term variance and so many other variants. For example, the figure below shows the variance of two models to compare. Is ...
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Why is Data with an Overrepresented Class called Imbalanced not Unbalanced?
I've seen the term Imbalanced used to described data that has an over-representation of one class.
What's the reasoning behind naming this type of data Imbalanced as opposed to Unbalanced, which seems ...
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Taxonomy of symbolic learning
Preliminaries
Broadly, the statistical learning approaches can be separated between parametric and non-parametric, and this is also the case for machine learning (ML) approaches. The taxonomy is well-...
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Is there a term for data based on calculations of raw data?
I'm trying to find out if there is a specific term for describing data created from computations based on raw data.
For example, I have the two data tables below. I'd like to know the term used to ...
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Meaning of 'Closed Form'
Here's an excerpt from a paper explaining Logistic Regression. What does 'Closed Form' mean in this context? Please explain in simple terms. The definitions online are confusing.
Gradient of Log ...
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Categories of time series
I am trying to classify different kinds of time series, but find myself missing good vocabulary. I don't mean that, for a given time series, I try to classify its datapoints into clusters or the like. ...
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Difference between prototype and centroid
Are these two terms "prototype" and "centroid" exchangeable? I know prototypes can be calculated using the mean of the features. Is it the same for centroid?
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difference between novelty, concept drift and anomaly
Concept drift is when the relation between the input
data and the target variable changes over time. like changes in the conditional distribution.
is novelty an outlier? what should I think of?
what ...
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Is sensitivity the same as recall in multiclass classification?
In Wikipedia, it is stated "In binary classification, recall is called sensitivity" under the Recall section. Are they both different in case of multi-class classification?
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What do "Under fitting" and "Over fitting" really mean? They have never been clearly defined
I am always getting lost when dealing with these terms. Especially being asked questions about the relationship such as underfitting-high bias (low variance) or overfitting-high variance (low bias).
...
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Difference between ethics and bias in Machine Learning
I'm confused about the difference between "ethics" and "bias" when those concepts are discussed in the context of Machine Learning (ML). In my understanding, ethical issue in ML is ...
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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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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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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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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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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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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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Decision trees vs Oblique decision trees
What are oblique decision trees ?
What are the differences between them and classic decision trees ?
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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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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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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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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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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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Loss function in GradientBoostingRegressor
Scikit Learn 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 ...
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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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