Leave One Out Cross Validation in Machine Learning | LOOCV
Leave One Out Cross Validation in Machine Learning | LOOCV
#crossvalidation #loocv #technologycult #machinelearning #random_state
#cross_val_score
Cross Validation is a statistical method used to estimate the performance or accuracy of the machine learning models.
In a typical machine learning task, we have to properly identify the problem so that we pick the most suitable algorithm which gives us the best score.
But how do we find the best possible score?
So in order to achieve the above we have to train the model with the available dataset and then we need to find how well our model performed on the dataset that we have trained.
However this is not the good practice.
Types of Cross-Validations
1. Non-Exhaustive Technique
2. Exhaustive Techniques
Exhaustive Cross-Validation methods are cross-validation methods which learn and test on all positive ways to divide the original sample into a training and a validation set
Non-Exhaustive Cross-Validation methods do not compute all ways of splitting the original sample. Those methods are approximations of leave-p-out cross-validations.
Types of Cross-Validation Methods
Non-Exhaustive Technique
a. Hold-Out
b. K-Fold
c. Stratified K-Fold
2. Exhaustive Techniques
a. Leave One Out Cross Validation
b. Leave P Out Cross Validation
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