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.

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a field of computer science, artificial intelligence, and linguistics concerned with the interactions between computers and human (natural) languages. As such, NLP…
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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 …
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to score or rate the performance of a model, most commonly with a metric like accuracy.
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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…
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a high-level language and interactive programming environment for numerical computation and visualization developed by MathWorks. Don’t use both the [matlab] and [octave] tags, unless the qu…
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the process of comparing several models and their respective results to choose the model is best according to some evaluation metric.
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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…
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For questions which concern getting started in Data Science or any of its related subdomains.
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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…
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For questions regarding outliers or unusual points in the data.
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Naive Bayes classifiers makes the naive assumption that the features are independent. They make use of Bayes theorem.
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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.
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an Apache open-source project that provides software for reliable and scalable distributed computing. The project itself includes a variety of other complementary additions.
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a Python library for computational linguistics.
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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®.
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Inclusion of additional constraints (typically a penalty for complexity) in the model fitting process. Used to prevent overfitting / enhance predictive accuracy.
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For question regarding distance between distributions or variables, such as Euclidean distance between points in n-space.
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an area of study concerning with retrieving documents, information or metadata from a collection of unstructured or semi-structured data.
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In machine learning, ensemble methods combine multiple algorithms to make a prediction. Bagging, boosting, and stacking, are some examples.
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the kind of parameters that cannot be directly learned during training but are set beforehand. Hyperparameters can define, for example, the complexity of the model or it…
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The Spark Python API (PySpark) exposes the apache-spark programming model to Python.
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A model-free reinforcement learning technique.