What's currently supported?

This article will help you understand what type of problems and machine learning frameworks are supported by the Quality Analysis.

Frameworks

The supported frameworks are Tensorflow's (Keras) sequential and model API; and Scikit-Learn's gradient boosting, logistic regression, and random forest (Classifier and Regressor).

Input data shape

Snitch currently supports tabular datasets (2D) for the training set and column (1D) or tabular (2D) datasets for the output dataset. In other words, Snitch supports classical machine learning classification or regression problems with single or multi-output. 

Saving methods

The supported saving methods are:

Library Code   Extension Link to documentation
Keras/Tensorflow model.save(file_path)   .h5 Tensorflow: Save and Serialize
Scikit-Learn joblib.dump(model, file_path)
pickle.dump(model, file_path)
  .joblib or .pkl Scikit-Learn: Model Persistence

String/categorical features

Models expecting data or targets to string are not supported. You should encode categorical features and targets as numerical values before training your model if you eventually want to perform a Quality Analysis. Preprocessing techniques like OneHotEncoding can be used to do so.

Classification targets

For classification problems, either class (0,1,2,...) or logits ( (1,0,0), (0,1,0), (0,0,1) ) as targets are accepted.

Time series

Time series are supported as long as they are formatted into tabular datasets (i.e., not sequential or 3D). Datetime values are however not supported. See this article for more information on how to format your dataset to use Snitch.

Natural language processing (NLP) and computer vision

Snitch does not support Natural Language Processing (NLP) and Computer Vision models.

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