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Just type, hit search, choose and download!A dictionary with an entry per field in the dataset used to build the deepnet. Whether alternate layers should learn a representation of the residuals for a given layer rather than the layer itself or not.
Complete information of the network. The key is the name of the algorithm used.
Whether to learn a tree-based representation of the data as engineered features along with the raw features, essentially by learning trees over slices of the input space and a small amount of the training data. Each layer is a map, and its structure will vary depending on the structure of the layers.
This includes per-node class names for classification problems and distribution information of the objective for regression problems. A list of maps, each one of which is a preprocessor, specifying one input feature to the network.
This layer may comprise binary encoding, normalization, and feature selection, as there may be less preprocessors than features in the original data. A status code that reflects the status of the deepnet creation.
Number of milliseconds that BigML took to process the deepnet. Example: 1 combiner optional Specifies the method that should be used to combine predictions in a non-boosted ensemble. For classification ensembles, the combination is made by majority vote. The options are: 0: plurality weights each model's prediction as one vote. You can set up both using the threshold argument.
If there are less than k models voting class, the most frequent of the remaining categories is chosen, as in a plurality combination after removing the models that were voting for class. The confidence of the prediction is computed as that of a plurality vote, excluding votes for the majority class when it's not selected. For regression ensembles, the predicted values are averaged. For a logistic regression, input data for all numerical fields except the objective field must be provided. Example: "my new prediction" private optional Whether you want your prediction to be private or not.
This will be 201 upon successful creation of the prediction and 200 afterwards. Make sure that you check the code that comes with the status attribute to make sure that the prediction creation has been completed without errors. The method used to combine predictions from the non-boosted ensemble. See the available combiners above. However, for logistic regressions, it really means probability, and thus, confidence will be deprecated soon.
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Note that this property is not available for ensembles with boosted trees and that for models An array of confidence pairs for each category in the objective field. True when the prediction has been created in the development mode. The number of predictions in the ensemble that failed. The dictionary of input fields' ids and values used as input for the prediction. Specifies the type of strategy that a model or models in an ensemble will follow when a missing value needed to continue with inference in the model is found.
Either 0, 1, or 2 to specify respectively whether the prediction is from a single model, an ensemble, or a logistic regression.
The id of the field that it predicts in the model, ensemble, or logistic regression. A string if the task is classification, a number if the task is regression prediction filterable, sortable A dictionary keyed with the objective field to get the prediction output for the model, ensemble, or logistic regression. An array with a prediction object for each model in the non-boosted ensemble.
An array of probability pairs for each category in the objective field. The parameters (k and class) given when a threshold-based combiner is used for the non-boosted ensemble. A list of the confidence (or expected error in the regression non-boosted ensemble) for each prediction candidate. Bad fields are ignored. That is, if you submit a value that is wrong, a prediction is created anyway ignoring the input field with the wrong value. An ordered array of Predicate Objects in the decision path from the root to the current node or to a final decision if the the next predicate array is empty.
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