Model Performance | Accuarcy | RMSE | Machine Learning

#crossvalidation #modelperformance #machinelearning

In this video we will discuss few of the common mistakes often made while performing cross validation of Machine Learning Models. While root means square error and accuracy rate are the two most popular metrics used in evaluating model performance in cross validation, there are limitations of using these when the performance is more important for the researcher in one section of the data than the other

For example we could be interested in better performance in predicting house price of a segment of the sample (say the middle priced houses) than the other segments. Similarly, we could be interested in predicting more default customers than the non default customer in a classification set up

For Study Packs : http://analyticuniversity.com/
Complete Data Science Course : http://bit.ly/34Sucmb
Access All Coursera Plus courses @ $400 : https://bit.ly/2ZL51Dd
Discounted courses on Udemy (for $11): http://bit.ly/2LYU6hp
Free access to Skillshare: http://bit.ly/2thklJu

Coursera :
Data Science : http://bit.ly/37nABr6
Data Science Python : http://bit.ly/2ZK5oMm

Recommended Data Science Books on Amazon :
Python for Data Science: https://geni.us/PythonDataScience
R for Data Science : https://geni.us/DataScienceR
Machine Learning using Tensorflow: https://geni.us/MLinTensorflow
Data Science from Scratch: https://geni.us/DataSciencefromScratch
Python programming: https://geni.us/LearnPython
Artificial Inteligence: https://geni.us/LearnAI
Data Vizualization : https://geni.us/DataViz

20% discounts on below live courses : use coupon YOUTUBE20
Data Science Live Training :
AI and Tensorflow: http://bit.ly/2tOnOzA
Python : http://bit.ly/2QkH1QQ