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In 1992, he published a book, ominously titled 1994.The list of fields's ids that were excluded to build the logistic regression. The list of input fields' ids used to build the logistic regression. It includes a list of coefficients and the field's dictionary describing the fields and their summaries. See here for more details. Specifies the list of ids of the field that the logistic regression predicts. In a future version, you will be able to share logistic regressions with other co-workers or, if desired, make them publicly available.

The alternative key that gives read access to this logistic regression. This is the date and time in which the logistic regression was updated with microsecond precision.

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Statistical tests to assess the quality of the model's fit to the data. This makes each field have roughly equivalent influence. Example: true category optional The category that best describes the cluster. G-means iteratively takes existing clusters and tests whether the cluster's neighborhood appears Gaussian. If it doesn't the cluster is split into two. The default is to 5, which seems to work well in most cases.

A range of 1 - 10 is acceptable. Specifies the fields that won't be included in the cluster. Example: true name optional The name you want to give to the new cluster. Example: l1 replacement optional Whether sampling should be performed with or without replacement. A dictionary that maps cluster ids to dataset resources offering per field distribution summaries for each cluster. Each dataset resource can be serialized on-demand using the neighborhood of the cluster. With no seed, the cluster locations can vary from run to run.

With a seed, the clusters are deterministic. All the information that you need to recreate or use the cluster on your own. It includes: clusters: a list of centroids with a cluster object for each centroid.

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It also contains the distance distribution of the entire dataset to that center. This will be 201 upon successful creation of the cluster and 200 afterwards.

Make sure that you check the code that comes with the status attribute to make sure that the cluster creation has been completed without errors. This is the date and time in which the cluster was created with microsecond precision.

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In a future version, you will be able to share clusters with other co-workers or, if desired, make them publicly available. This is the date and time in which the cluster was updated with microsecond precision.Example: "description": "This field is a transformation" descriptions optional A description for every of the new fields generated. Example: "fields": "(window Price -2 0)" label optional Label of the new field.

Example: "label": "New price" Labels for each of the new fields generated. Example: "name": "Price" names optional Names for each of the new fields generated.

Example: "This is a description of my new sample" name optional The name you want to give to the new sample.

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This will be 201 upon successful creation of the sample and 200 afterwards. Make sure that you check the code that comes with the status attribute to make sure that the sample creation has been completed without errors and that it is still available in the in-memory cache. This is the date and time in which the sample was created with microsecond precision. True when the sample has been created in the development mode. In a future version, you will be able to share samples with other co-workers.

It includes the fields' dictionary describing the fields and their summaries and the rows. A description of the status of the sample. This is the date and time in which the sample was updated with microsecond precision. Each entry includes the column number in the original dataset, the name of the field, the type of the field, and the summary. See this Section for more details. A list of lists representing the rows of the sample. Values in each list are ordered according to the fields list.

A status code that reflects the status of the sample creation. That is no categories are specified. A dictionary between input field id and an array of categories to limit the analysis to. Each array must contain 2 or more unique and valid categories in the string format.

If omitted, each categorical field is limited to its 100 most frequent categorical values. This field has no impact if the data type of input fields are non-categorical. Example: "This is a description of my new correlation" Global numeric field transformation parameters.

Example: false size optional The number of equal width bins. If pretty is enabled then this value acts as a maximum size, but the actual number of bins may be lower. Example: 12 trim optional A real number between 0 and 0. Default is 0, however, 0.