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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.

the process of using domain knowledge of the data to create features that improve machine learning algorithms
Refers to general procedures that attempt to determine the generalizability of a statistical result. Cross-validation arises frequently in the context of assessing how a particular model fit predicts …
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.
629 questions
Area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.
618 questions
Support Vector Machines (SVM) are a popular supervised machine learning algorithm that can be used for classification or regression.
579 questions
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…
574 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…
559 questions
Questions referring to classifiers or classifying problems where some of the classes in the data are under-represented.
558 questions
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.
Data preprocessing is a data mining technique that involves transforming raw data into a better understandable or more useful format.
533 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…
500 questions
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…
499 questions
Everything related to recommender systems
485 questions
Use for questions related to the Transformer (based on encoder-decoder) architecture in machine learning.
479 questions
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 …
457 questions
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
Finding hidden (statistical) structure in unlabelled data, including clustering and feature extraction for dimensionality reduction.
455 questions
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.
451 questions
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…
449 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…
415 questions
In data science, accuracy is a measurement used to determine which model is best at describing the underlying patterns of a dataset.
408 questions
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…
405 questions
A measure of the degree of linear association among a pair of variables.
401 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…
397 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 …
382 questions
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…
meant to be used for questions related to how to evaluate a model performance, not only based on standard metrics, but also in the context of real use case applications. What is a good mod…
368 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…
362 questions
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.
361 questions
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.
BERT stands for Bidirectional Encoder Representations from Transformers and is designed to pre-train deep bidirectional representations by jointly conditioning on both left and right context in all l…
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 …
353 questions
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…
346 questions
Forecasting is the process predicting future values based on historic and current data, typically for time-series datasets.
344 questions
Autoencoders are a type of neural network that learns a useful encoding for data in an unsupervised manner.
339 questions
Principal component analysis, a technique for dimensionality reduction.
336 questions
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