A tag is a keyword or label that categorizes your question with other, similar questions. Using the right tags makes it easier for others to find and answer your question.

Refers to a subset of data mining concerned with extracting information from data in the form of text by recognizing patterns. The goal of text mining is often to classify a given document into one of…
554 questions
the process of using domain knowledge of the data to create features that improve machine learning algorithms
Computer Vision is a subfield of computer science which deals with analyzing and understanding images. This includes detection of objects like faces in images or segmenting images.
Support Vector Machines (SVM) are a popular supervised machine learning algorithm that can be used for classification or regression.
Pytorch is an open source library for Tensors and Dynamic neural networks in Python with strong GPU acceleration. For details, see https://pytorch.org.
Questions referring to classifiers or classifying problems where some of the classes in the data are under-represented.
A function used to quantify the difference between observed data and predicted values according to a model. Minimization of loss functions is a way to estimate the parameters of the model.
470 questions
Multi-class classification is when you have a classification problem with multiple classes, specifically 3 or more classes. Many classifications are binary by design, therefore the additional nomencla…
Big data is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The ch…
456 questions
Data preprocessing is a data mining technique that involves transforming raw data into a better understandable or more useful format.
In statistics this refers to selecting an estimator of a parameter by maximizing or minimizing some function of the data. One very common example is choosing an estimator which maximizes the joint den…
Everything related to recommender systems
419 questions
Finding hidden (statistical) structure in unlabelled data, including clustering and feature extraction for dimensionality reduction.
k-means is a family of cluster analysis methods in which you specify the number of clusters you expect. This is as opposed to hierarchical cluster analysis methods.
412 questions
Word embedding is the collective name for a set of language modeling and feature learning techniques in NLP where words are mapped to vectors of real numbers in a low dimensional space, relative to th…
Gradient Descent is an algorithm for finding the minimum of a function. It iteratively calculates partial derivatives (gradients) of the function and descends in steps proportional to those partial d…
prediction is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has …
An algorithm is a set of one or more computations that will produce a calculated result. All statistics methods are algorithms. Algorithms can be simple, such as calculating a percentage, or can be ve…
382 questions
Variables (used for prediction or explication) used in regression or regression-like models (like clustering, discrimination). Use this tag for questions about constructing such variables or selecting…
361 questions
Categorical data can take on a limited (usually fixed) number of possible values called categories. Categorical values "label", they do not "measure". Nominal and dichotomous/binary scale types are ca…
A measure of the degree of linear association among a pair of variables.
344 questions
Supervised learning is a type of machine learning algorithm that learns a mapping function y = f(x) between input variables (x) and output variables (y). The two most common supervised learning tasks …
343 questions
word2vec is a two layer neural network to process text. It takes words as an input and outputs a vector correspondingly. It uses a combination of Continuous Bag of Word and skipgram model implementati…
In data science, accuracy is a measurement used to determine which model is best at describing the underlying patterns of a dataset.
Object detection is a computer-vision and image-processing technique for locating instances of objects in images or videos. Common applications include face detection and object tracking. Object detec…
NumPy is one of the fundamental packages used for scientific computing in the Python ecosystem. Specifically, NumPy is used for numerical computing and uses n-dimensional arrays.
Autoencoders are a type of neural network that learns a useful encoding for data in an unsupervised manner.
Principal component analysis, a technique for dimensionality reduction.
Anomaly detection refers to the problem of finding patterns in data that do not conform to expected behaviour. This is also known as outlier detection.
A data frame is a tabular data structure. Usually, it contains data where rows are observations and columns are variables of various types. While "data frame" or "dataframe" is the term used for this …
Modeling error (especially sampling error) instead of replicable and informative relationships among variables improves model fit statistics, but reduces parsimony, and worsens explanatory and predict…
296 questions
Multilabel classification assigns to each sample a set of target labels. This can be thought as predicting properties of a data-point that are not mutually exclusive, such as topics that are relevant …
284 questions
Use for questions about Backpropagation, which is commonly used in training Neural Networks in conjunction with an optimization method such as gradient descent.
277 questions
For use when discussing the commutative and linear, but not associative operator interpreted on functions and distributions.
277 questions
Dimensionality reduction refers to techniques for reducing many variables into a smaller number while keeping as much information as possible. One prominent method is [tag pca]
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