Topic: snapml Goto Github
Some thing interesting about snapml
Some thing interesting about snapml
snapml,Python scripts exploring machine learning algorithms with libraries like scikit-learn and snapml. Great for beginners!
User: 1024ent
snapml,Dectecting chronic heart disease using machine learning 100%
User: kmohamedalie
Home Page: https://github.com/Kmohamedalie/Chronic-Kidney-Disease
snapml,Alphabet recognition
User: kmohamedalie
Home Page: https://github.com/Kmohamedalie/Letter_Recognition/tree/master
snapml,Recognizing handwritten digits with classical machine learning with a 97% accuracy and f1-score
User: kmohamedalie
Home Page: https://github.com/Kmohamedalie/MNIST/tree/master
snapml,Recognising Handwritten digits with 98.89% accuracy, precision and f1-score
User: kmohamedalie
Home Page: https://github.com/Kmohamedalie/Optical-Recognition-of-Handwritten-Digits
snapml,Prediction bankruptcy of Taiwanese bank in the years 1999 to 2009.
User: kmohamedalie
Home Page: https://github.com/Kmohamedalie/Taiwenese-Bankruptcy-Prediction
snapml,SnapML library's Decision Tree classifier and SVM was used to train a model on a real dataset to identify fraudulent credit card transactions. The Decision Tree model resulted in ROC-AUC score = 0.92 and the SVM yielded ROC-AUC score = 0.93 and hinge loss = 0.15. Multi-threaded CPU was implemented to reduce model training time.
User: sharmeen-k
snapml,A decision tree regressor from SnapML by IBM was used to predict taxi tips on a dataset from the NYC TL Commission. The model was trained on over 3 million data samples in 0.636 seconds using multithreaded CPU/GPU acceleration. Achieved mean squared error = 1.62 on test data.
User: sharmeen-k
snapml,Detecting the fraudulent credit card transactions by training Decision Tree model using Scikit-learn and SnapML
User: vyjayanthipolapragada
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