python code for Keras Deep NN Confidence Interval for regression using

data is tabular with both categorical and continues features target is continues so it is regression you need to simulate data or find existing data set data you create by yourself or you existing data set but both categorical and continues features data is not big - lets say 100000 rows and 18 features 8 features categorical and 8 features continues many values for each categorical feature you need some special way to find reliability for each predictions but use many time runs with different random seed - use something better and smarter for example do not use this approach [login to view URL] [login to view URL] since approach is weak one you need to find better solution maybe [login to view URL] [login to view URL] [login to view URL]@qucit/a-simple-technique-to-estimate-prediction-intervals-for-any-regression-model-2dd73f630bcb [login to view URL] [login to view URL]:1472065/[login to view URL] page 14 2.4.4 Prediction Interval for Artificial Neural Networks Several methods to estimate prediction intervals for neural networks exist. These include dropout [4], bootstrap [23], mean variance estimation [23] and upper lower bound estimation [24]. [login to view URL] [login to view URL]@qucit/a-simple-technique-to-estimate-prediction-intervals-for-any-regression-model-2dd73f630bcb algorithm from these links maybe not good enough, you need to find theory and develop code to provide good solution