Dark Matter
Ensemble AI's Core IP algorithm for generating highly predictive embeddings for any machine learning dataset.
Last updated
Ensemble AI's Core IP algorithm for generating highly predictive embeddings for any machine learning dataset.
Last updated
The total number of input features.
The number of desired output "features" (embedding length). Must be a number between 1 and 500, inclusive.
The total number of targets. Currently, only 1 is supported.
One of "regression" or "classification".
The input data feature set.
The input data targets.
Enable/disable explainability mode.
Optional feature names for use with explainability mode. If none are specified, either the names are inferred from the X argument if it is a Pandas DataFrame or will be generic names ["feature_0", "feature_1", ...].
Optional string for use to save algorithm weights to disk. The default is a "YY-MM-DD_hh-mm-ss" string based on the environment clock.
The total number of training iterations to use during algorithm fitting. This must be greater than the sensitivity argument.
The number of training examples to use for each batch during fitting. Floating point arguments must be a number between 0 and 1 to indicate the percentage of the input data to be used as a batch.
Fits the algorithm to the input data.
Transforms a given dataset to embeddings using Dark Matter.
Saves the algorithm weights to a subfolder "{path}/{project_name}/"
.
Loads the algorithm's weights from a subfolder "{path}/{project_name}/"
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NaN values are not compatible with algorithm training, these must be filled prior to calling fit or generate.
Dark Matter utilizes a variety of Data Science and backend Python packages for algorithm training and source code management. Learn more at The Dark Matter Environment.
GPU support.
Training progress and insights.
Automatic hyperparameter tuning.