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.

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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Text is a type of data often used in data science projects involving natural language processing.
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Language models are used extensively in Natural Language Processing (NLP) and are probability distributions over a sequence of words or terms.
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Graphics Processing Units (GPUs) within the context of Machine Learning often refer to the hardware requirements, design considerations, or level of parallelization for implementing and running variou…
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Jupyter is a collection of environments and protocols for interactive computing. It supports many languages and kernels, and works with frontends including the web application Jupyter Notebook. Jupyte…
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Image preprocessing are the steps taken to format images before they are used by model training and inference. This includes, resizing, orienting, and color corrections. Preprocessing is required t…
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MATLAB is 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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Use for questions about graphing a dataset into a visualisation (e.g. line plot, histogram or pie chart). Visualisation and plotting is an important EDA tool as well as presenting the results of a dat…
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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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For questions which concern getting started in Data Science or any of its related subdomains.
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A topic model describes text from a large corpus as a probability distribution over topics which are probability distributions over words. There are quantified contributions from all topics to a speci…
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Hyperparameters of a model are 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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Information Retrieval is an area of study concerning with retrieving documents, information or metadata from a collection of unstructured or semi-structured data.
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Bayesian statistics is a statistical paradigm that contrasts with that of frequentist statistics. Bayesian methods rely on prior information do determine the degree of belief in the probability of a v…
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NLTK is a free, open-source natural language processing toolkit for python. It is used primarily for text processing applications and includes libraries specifically made for classification, tokenizat…
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tf–idf (term frequency–inverse document frequency), is a numerical statistic using in nlp that is intended to reflect how important a word is to a document in a collection or corpus. It is often used…
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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.
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Latent Dirichlet Allocation (LDA) is an algorithm in the field of topic modeling.
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Data imputation is the process of replacing missing data with substituted values. This could involve statistically representative data filling (e.g. local averages) or simply replacing the missing dat…
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Kaggle is an online community for data scientists and machine learning practitioners owned by Google.
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YOLO, or You Only Look Once, is a real-time object detection algorithm
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Ranking is ordering data from highest to lowest or *vice versa.* For questions about *constructing* scores to use in ranking, please use the "valuation" tag, too.
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Hadoop is 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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