Day #88 - Metrics Optimization

Loss vs Metric

• Metric - Function which we want to use to evaluate the model. Maximum accuracy in classification
• Optimization Loss - Easy to optimize for given model, Function our model optimizes. MSE, LogLoss
• Preprocess train and optimize another metric - MSPE, MAPE, RMSLE
• Optimize another metric postprocess predictions - Accuracy, Kapps
• Early Stopping - Stop traning when models starts to overfit

 Custom loss functions

Accuracy Metrics

	def logregobj(preds, dtrain):
	labels = dtrain.get_label()
	preds = 1.0/(1.0+np.exp(-preds))
	grad = preds-labels
	hess = preds*(1.0-preds)
	return grad, hess

view raw customloss.py hosted with ❤ by GitHub

Happy Coding and Learning!!!