Many times we get in a dilemma of which machine learning model should we use for a given problem. KFold cross validation allows us to evaluate performance of a model by creating K folds of given dataset. This is better then traditional train_test_split. In this tutorial we will cover basics of cross validation and kfold. We will also look into cross_val_score function of sklearn library which provides convenient way to run cross validation on a model
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Exercise: Exercise description is avialable in above notebook towards the end
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Topics that are covered in this Video:
0:00 Introduction
0:21 Cross Validation
1:02 Ways to train your model( use all available data for training and test on same dataset)
2:08 Ways to train your model( split available dataset into training and test sets)
3:26 Ways to train your model (k fold cross validation)
4:26 Coding (start) (Use hand written digits dataset for kfold cross validation)
8:23 sklearn.model_selection KFold
9:10 KFold.split method
12:21 StratifiedKFold
19:45 cross_val_score
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Machine Learning Tutorial Python 12 - K Fold Cross Validation
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