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Cross_Validation in machine learning.

 we all are aware about the machine learning steps that we follows. one very important step is to check the performance of the machine learning model in unseen data(not used while training).

This is called cross validation techniques.


to understand this in detail please cover till model selection. so now we are in cross validation step. what we are going to do. we are all aware that we do train test split. what is this, i am not going to teach you in detail here but yes giving you the rough idea about this. we divide the complete datasets into 2 parts. before model selection or u can say model fitment. these two parts are training datasets and testing datasets. in training datasets, we do fit the machine learning model and same we do testing in test datasets to check either machine learning model working fine with datasets or not.

it is not possible to complete the cross validation without video so i have created one video on cross validation that cover all types and parts in cross validation.

here i am discussing about process that we follow in machine learning so that we come to know where to use cross validation and why to use.

1-data gathering.

2-joinig, merging, cleaning etc.

3-EDA and feature engineering.

4-separate data in dependent and independent variables. i.e x and y

5-spliting datasets into training and testing.

6-seleting of machine learning and fit into training datasets.

7-model evaluation according to regression and classification datasets.

8-check accuracy score.( here we use cross validation and check score)

so, what actually we do here, as name suggest cross- means randomly and validation means verify.

lets, understand with examples-we have 100 record datasets for evaluation and got accuracy score is 95.7. 






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