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How to know the names of featurecollection columns?
Knowing the names and dataypes of FeatureCollection columns can be helpful (e.g., filtering a collection by metadata ). The following example prints column names and datatypes for a collection of point features representing protected areas. // Import a protected areas point feature collection.
Can a featurecollection be retrieved from a disk?
Additionally, collections not retrieved from disk (“computed collections”, like those derived from lists or reduceRegions ()) may lack meaningful type information. For more general purpose FeatureCollection aggregation tools, see the Reducing a FeatureCollection page.
How are feature collections used in Google Earth?
Groups of related features can be combined into a FeatureCollection, to enable additional operations on the entire set such as filtering, sorting and rendering. Besides just simple features (geometry + properties), feature collections can also contain other collections.
How to get information from feature collection metadata?
Methods for getting information from feature collection metadata are the same as those for image collections. See the ImageCollection Information and Metadata section for details. You can use the aggregation shortcuts to count the number of features or summarize an attribute:
How to reduce the properties of a featurecollection?
To reduce properties of features in a FeatureCollection, use featureCollection.reduceColumns (). Consider the following toy example: // Make a toy FeatureCollection. // Compute a weighted mean and display it. Note that the inputs are weighted according to the specified weight property. The result is therefore:
Can a reference be modified for different collections?
The reference examples shown on this page may be modified for collections requiring more or less specific information to locate materials, for different types of collections, or for additional descriptive information (e.g., a translation of a letter).
How is dimensionality reduction different from feature selection?
While both methods are used for reducing the number of features in a dataset, there is an important difference. Feature selection is simply selecting and excluding given features without changing them. Dimensionality reduction transforms features into a lower dimension.
Is there a way to reduce a featurecollection?
For more general purpose FeatureCollection aggregation tools, see the Reducing a FeatureCollection page. Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies.
How is feature elimination used in feature selection?
Recursive feature elimination fits a model and removes the weakest features until the specified number of features is reached. The top 5 features under recursive feature elimination are: These features are slightly different to those selected under univariate feature selection. Let us now test the model accuracy.
Where are column names and values located in a dictionary?
// dictionary where keys are column names and values are datatypes. Note: Computed collections may not have column information available as part of the collection metadata. Metadata exists for collections within the Data Catalog and those stored as assets. During some operations, however, metadata can be lost (joins, mapping, etc.).