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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.

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 …
322 questions
Use for questions about Backpropagation, which is commonly used in training Neural Networks in conjunction with an optimization method such as gradient descent.
307 questions
Probability is the branch of mathematics concerning numerical descriptions of how likely an event is to occur or how likely it is that a proposition is true.
302 questions
Normalization is a technique often applied as part of data preparation for machine learning. The goal of normalization is to change the values of numeric columns in the dataset to use a common scale, …
296 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]
291 questions
Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.
290 questions
For questions about text classification, the task of assigning predefined categories (or classes) to free-text documents.
289 questions
Hyperparameter tuning (also called hyperparameter optimization) refers to the process of finding the optimal set of hyperparameters for a given machine learning algorithm.
287 questions
For use when discussing the commutative and linear, but not associative operator interpreted on functions and distributions.
286 questions
Feature scaling is a data pre-processing step where the range of variable values is standardized. Standardization of datasets is a common requirement for many machine learning algorithms. Popular feat…
263 questions
Model selection is the process of comparing several models and their respective results to choose the model is best according to some evaluation metric.
255 questions
A form of signal processing where the input is an image. Usually treating the digital image as a two-dimensional signal (or multidimensional). This processing may include image restoration and enhance…
251 questions
A metric is a way to evaluate the performance of a machine learning model. Depending on the task, different metrics may be used.
251 questions
Apache Spark is an open source cluster computing system that aims to make data analytics fast — both fast to run and fast to write, originally developed in the AMPLab at UC Berkeley.
242 questions
Mathematics in a data science or machine learning context refers to the mathematical underpinnings for algorithms, optimization, statistics, and linear algebra etc.
GAN refers to Generative Adversarial Networks. Such networks is made of two networks that compete against each other. The first one generates new samples and the second one discriminates between ge…
238 questions
Sentiment analysis refers to categorizing some given data as to what sentiment(s) it expresses. Usually, it refers to extracting sentiment from a text, e.g. tweets or blog posts.
236 questions
Use for questions about Orange, the free, open-source, component-based, data mining and machine learning software suite.
235 questions
For questions regarding outliers or unusual points in the data.
224 questions
For the nodes and links sense of graph; use the visualization tag for the charting sense.
219 questions
Matplotlib is a plotting library for Python which may be used interactively or embedded in stand-alone GUIs. Its compact "pyplot" interface is similar to the plotting functions of MATLAB®.
214 questions
Naive Bayes classifiers makes the naive assumption that the features are independent. They make use of Bayes theorem.
205 questions
Missing data is a problem that arises in data science when some data contained in rows or columns may be missing or unavailable for some samples in a dataset.
204 questions
K-Nearest Neighbor (K-NN) is a classification algorithm that determines the label of some data point based on the most common label of the closest k other points.
200 questions
In machine learning, ensemble methods combine multiple algorithms to make a prediction. Bagging, boosting, and stacking, are some examples.
198 questions
Transfer learning is the process of learning a set of characteristics from one data and applying this "knowledge" to another similar dataset (i.e. using the same model across datasets).
197 questions
For questions about models designed for generating new data (or generating samples from a probability distribution).
Encoding in machine learning and data science refers to the process by which non-numeric data is transformed into a numeric representation that can be fed into machine learning algorithms.
187 questions
A confusion matrix is a special contingency table used to evaluate the predictive accuracy of a classifier. Predicted classes are listed in rows and actual classes in columns, with counts of respectiv…
182 questions
For Question about Performance of a data science, statistical or machine learning model. Performace is a direct way to measure the efficiency of model. The Performance measure deals with time, accurac…
181 questions
Activation function is a non-linear transformation, usually applied in neural networks to the output of the linear or convolutional layer. Common activation functions: sigmoid, tanh, ReLU, etc.
174 questions
Inclusion of additional constraints (typically a penalty for complexity) in the model fitting process. Used to prevent overfitting / enhance predictive accuracy.
172 questions
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