Identifying which category an object belongs to.
Applications: Spam detection, image recognition.
Algorithms: Gradient boosting, nearest neighbors, random forest, logistic regression, and more...
Predicting a continuous-valued attribute associated with an object.
Applications: Drug response, stock prices.
Algorithms: Gradient boosting, nearest neighbors, random forest, ridge, and more...
Automatic grouping of similar objects into sets.
Applications: Customer segmentation, grouping experiment outcomes.
Algorithms: k-Means, HDBSCAN, hierarchical clustering, and more...
Reducing the number of random variables to consider.
Applications: Visualization, increased efficiency.
Algorithms: PCA, feature selection, non-negative matrix factorization, and more...
Comparing, validating and choosing parameters and models.
Applications: Improved accuracy via parameter tuning.
Algorithms: Grid search, cross validation, metrics, and more...
Feature extraction and normalization.
Applications: Transforming input data such as text for use with machine learning algorithms.
Algorithms: Preprocessing, feature extraction, and more...
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