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Datasets Last Updated: Monday, 2017-10-30 10:31 A dataset is a structured version of a source where each field has been processed and serialized according to its type. The possible field types are numeric, categorical, text, date-time, or items. For each field, you can also get the number of errors that were encountered processing it. Errors are mostly missing values or values that do not match with the type assigned to the column. When you create a new dataset, histograms of the field values are created for the categorical and numeric fields.
In addition, for the numeric fields, a collection of statistics about the field distribution such as minimum, maximum, sum, and sum of squares are also computed. For date-time fields, BigML attempts to parse the format and automatically generate the related subfields (year, month, day, and so on) present in the format. For items fields which have many different categorical values per instance separated by non-alphanumeric characters, BigML tries to automatically detect which is the best separator for your items.
We are then left with somewhere between a few dozen and a few hundred interesting words per text field, the occurrences of which can be features in a model. You can also list all of your datasets. The former specifies the list of fields to be included in the dataset, and defaults to all fields in the source when empty. That is, no names, labels or descriptions are changed.
Updates the names, labels, and descriptions of the fields in the dataset with respect to the original names in the source. An entry keyed with the field id generated in the source for each field that you want the name updated. All the fields in the source. Specifies the fields to be included in the dataset. The first element is an operator and the rest of the elements its arguments. See the section below for more details.
Specifies the default objective field.
Example: true size optional Integer,default is the source's size The number of bytes from the source that you want to use. Example: 500 You can also use curl to customize a new dataset with a name, and different size, and only a few fields from the original source. If you do not specify a size, BigML.
If you do not specify any fields BigML. This predicate is specified as a (possibly nested) JSON list whose first element is an operator and the rest of the elements its arguments. Here's an example of a filter specification to choose only those rows whose field "000002" is less than 3. Note how you're not limited to two arguments. It's also worth noting that for a filter like that one to be accepted, all three fields must have the same optype (e.So thrilled with my current results on the 14 day detox.
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