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Photometric classification of supernovae (SNe) is imperative as recent andupcoming optical time-domain surveys, such as the Large Synoptic SurveyTelescope (LSST), overwhelm the available resources for spectrosopic follow-up.Here we develop a range of light curve classification pipelines, trained on 518spectroscopically-classified SNe from the Pan-STARRS1 Medium-Deep Survey(PS1-MDS): 357 Type Ia, 93 Type II, 25 Type IIn, 21 Type Ibc, and 17 Type ISLSNe. We present a new parametric analytical model that can accommodate abroad range of SN light curve morphologies, including those with a plateau, andfit this model to data in four PS1 filters (griz). We test a number of featureextraction methods, data augmentation strategies, and machine learningalgorithms to predict the class of each SN. Our best pipelines result in 90%average accuracy, 70% average purity, and 80% average completeness for all SNclasses, with the highest success rates for Type Ia SNe and SLSNe and thelowest for Type Ibc SNe. Despite the greater complexity of our classificationscheme, the purity of our Type Ia SN classification, 95%, is on par withmethods developed specifically for Type Ia versus non-Type Ia binaryclassification. As the first of its kind, this study serves as a guide todeveloping and training classification algorithms for a wide range of SN typeswith a purely empirical training set, particularly one that is similar in itscharacteristics to the expected LSST main survey strategy. Future work willimplement this classification pipeline on ~3000 PS1/MDS light curves that lackspectroscopic classification.

Supernova Photometric Classification Pipelines Trained on Spectroscopically Classified Supernovae from the Pan-STARRS1 Medium-Deep Survey