Apply an imported model in TensorFlow or ONNX format.
Post-process data after it has been passed through a model.
The transformations in this guide return classes that implement the IEstimator interface. Data transformations can be chained together. Each transformation both expects and produces data of specific types and formats, which are specified in the linked reference documentation.
Some data transformations require training data to calculate their parameters. For example: the NormalizeMeanVariance transformer calculates the mean and variance of the training data during the Fit() operation, and uses those parameters in the Transform() operation.
Other data transformations don't require training data. For example: the ConvertToGrayscale transformation can perform the Transform() operation without having seen any training data during the Fit() operation.
Scale each value in a row by subtracting the mean of the row data and divide by either the standard deviation or l2-norm (of the row data), and multiply by a configurable scale factor (default 2)
Assign the input value to a bin index and divide by the number of bins to produce a float value between 0 and 1. The bin boundaries are calculated to evenly distribute the training data across bins
Map each input vector onto a lower dimensional feature space, where inner products approximate a kernel function, so that the features can be used as inputs to the linear algorithms
Transforms a binary classifier raw score into a class probability by assigning scores to bins, and calculating the probability based on the distribution among the bins
Transforms a binary classifier raw score into a class probability by assigning scores to bins, where the position of boundaries and the size of bins are estimated using the training data
Apply an expression to transform columns into new ones
No
Collaborate with us on GitHub
The source for this content can be found on GitHub, where you can also create and review issues and pull requests. For more information, see our contributor guide.
.NET feedback
.NET is an open source project. Select a link to provide feedback:
Manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Python, Azure Machine Learning and MLflow.