Faizanuddin Ansari

Faizanuddin Ansari

Senior Research Fellow

Cross Validation Techniques

less than 1 minute read

Published:

Cross validation is a method of estimating expected prediction error.

While choosing machine learning models, we need to compare models, to see how different models perform on our dataset, this goes beyond just comparing different models but also in explaining to the those who are working with you.

You needs to know how valid is your dataset or models that you’re working with . So, needs for cross validation comes up when data is usually limited and training and testing on the same portion of the data does not gives us an accurate view of how our model performs .

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