{
  "changeLog": "",
  "cpu": 0,
  "description":"Please train this ML Package before deploying it as it will not return anything otherwise.   \n\nTPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data. Once TPOT is finished searching (or you get tired of waiting), it provides you with the Python code for the best pipeline it found so you can tinker with the pipeline from there. TPOT is built on top of scikit-learn, so all the code it generates should look familiar to scikit-learn users.   \n\nThe model is based on a publication entitled \"Scaling tree-based automated machine learning to biomedical big data with a feature set selector.\" from Trang T. Le, Weixuan Fu and Jason H. Moore (2020) and \"Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science.\" from Randal S. Olson, Nathan Bartley, Ryan J. Urbanowicz, and Jason H. Moore. ",
  "displayName": "TPOTAutoMLClassification",
  "gpu": 0,
  "inputDescription":"Features used by the model to make predictions. For example: { \n\n“Feature1”:  12, \n\n“Feature2”:  222, \n\n. \n\n. \n\n“FeatureN”:  110 \n\n} ",
  "inputType": "JSON",
  "memory": 0,
  "mlPackageLanguage": "PYTHON36",
  "name": "TPOTAutoMLClassification",
  "outputDescription":"JSON with predicted class, associated confidence on that class prediction (between 0-1) and label name. Label names are returned only if the label encoding was performed by the pipeline, within AI Center. Some scikit-learn models do not support confidence scores. If the output of the optimization pipeline is a scikit-learn model which does not support confidence scores the output will only contain the predicted class. Ex: { \n\n  \"predictions\": 0,  \n\n  \"confidences\": 0.6, \n\n  \"labels\": “yes”  \n\n} \n\nOr if label encoding was done outside of the model: { \n\n  \"predictions\": 0,  \n\n  \"confidences\": 0.6, \n\n}   ",  "processorType": "CPU",
  "processorType": "CPU",
  "projectId": "[project-id]",  
  "retrainable": true,
  "stagingUri": "[staging-uri]",
  "projectName": "Tabular Data",
  "projectDescription": "Models for analyzing tabular data including classification and regression ML Packages",
  "tenantName": "Open-Source Packages",
  "minAIFabricVersion": "v21.10",
  "languageVersion": 0,
  "version": 1,
  "contentUri": "https://<placeholder-url>/publicmodels/AIC/TPOTAutoMLClassification/1/TPOTAutoMLClassification.zip"
}