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  2. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]

  3. Online machine learning - Wikipedia

    en.wikipedia.org/wiki/Online_machine_learning

    Online learning is a common technique used in areas of machine learning where it is computationally infeasible to train over the entire dataset, requiring the need of out-of-core algorithms. It is also used in situations where it is necessary for the algorithm to dynamically adapt to new patterns in the data, or when the data itself is ...

  4. Multiclass classification - Wikipedia

    en.wikipedia.org/wiki/Multiclass_classification

    Based on learning paradigms, the existing multi-class classification techniques can be classified into batch learning and online learning. Batch learning algorithms require all the data samples to be available beforehand. It trains the model using the entire training data and then predicts the test sample using the found relationship.

  5. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    Major advances in this field can result from advances in learning algorithms (such as deep learning), computer hardware, and, less-intuitively, the availability of high-quality training datasets. [1] High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to ...

  6. Multi-label classification - Wikipedia

    en.wikipedia.org/wiki/Multi-label_classification

    The online learning algorithms, on the other hand, incrementally build their models in sequential iterations. In iteration t, an online algorithm receives a sample, x t and predicts its label(s) ลท t using the current model; the algorithm then receives y t, the true label(s) of x t and updates its model based on the sample-label pair: (x t, y t).

  7. scikit-learn - Wikipedia

    en.wikipedia.org/wiki/Scikit-learn

    scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...

  8. Python (programming language) - Wikipedia

    en.wikipedia.org/wiki/Python_(programming_language)

    Python 3.13 introduces some change in behavior, i.e. new "well-defined semantics", fixing bugs (plus many removals of deprecated classes, functions and methods, and removed some of the C API and outdated modules): "The [old] implementation of locals() and frame.f_locals is slow, inconsistent and buggy [and it] has many corner cases and oddities ...

  9. fast.ai - Wikipedia

    en.wikipedia.org/wiki/Fast.ai

    In 2018, students of fast.ai participated in the Stanford’s DAWNBench challenge alongside big tech companies such as Google and Intel.While Google could obtain an edge in some challenges due to its highly specialized TPU chips, the CIFAR-10 challenge was won by the fast.ai students, programming the fastest and cheapest algorithms.