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Trains the Clusterer on a collection of features using the specified numeric properties of each feature as training data. The geometry of the features is ignored.
The list of property names to include as training data. Each feature must have all these properties, and their values must be numeric. This argument is optional if the input collection contains a 'band_order' property (as produced by Image.sample).
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-09-19 UTC."],[[["\u003cp\u003eTrains a clusterer using numeric properties of features, ignoring geometry.\u003c/p\u003e\n"],["\u003cp\u003eRequires a feature collection and optionally specifies input properties for training.\u003c/p\u003e\n"],["\u003cp\u003eAllows for subsampling of the training data using a factor and seed.\u003c/p\u003e\n"],["\u003cp\u003eReturns the trained Clusterer object for further use.\u003c/p\u003e\n"]]],["The `Clusterer.train` method trains a Clusterer using a FeatureCollection. It takes a collection of features and uses their numeric properties as training data, ignoring feature geometry. Users specify `inputProperties` (a list of numeric property names) to be used for training. Subsampling can be employed by setting the `subsampling` (factor between 0 and 1) and optionally, the `subsamplingSeed` to control randomness. The method returns the trained `Clusterer` object.\n"],null,["# ee.Clusterer.train\n\nTrains the Clusterer on a collection of features using the specified numeric properties of each feature as training data. The geometry of the features is ignored.\n\n\u003cbr /\u003e\n\n| Usage | Returns |\n|------------------------------------------------------------------------------------------|-----------|\n| Clusterer.train`(features, `*inputProperties* `, `*subsampling* `, `*subsamplingSeed*`)` | Clusterer |\n\n| Argument | Type | Details |\n|-------------------|---------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| this: `clusterer` | Clusterer | An input Clusterer. |\n| `features` | FeatureCollection | The collection to train on. |\n| `inputProperties` | List, default: null | The list of property names to include as training data. Each feature must have all these properties, and their values must be numeric. This argument is optional if the input collection contains a 'band_order' property (as produced by Image.sample). |\n| `subsampling` | Float, default: 1 | An optional subsampling factor, within (0, 1\\]. |\n| `subsamplingSeed` | Integer, default: 0 | A randomization seed to use for subsampling. |"]]
