K-fold cross-validation:
In machine learning, we couldn’t fit the model on the training data and can’t say that the model will work accurately for the real data. For this, we must assure that our model got the correct patterns from the data, and it is not getting up too much noise. For this purpose, we use the cross-validation technique.
Cross-validation is a technique in which we train our model using the subset of the data-set and then evaluate using the complementary subset of the data-set.
The three steps involved in cross-validation are as follows :
Reserve some portion of the sample data-set.
Using the rest data-set train the model.
Test the model using the reserve portion of the data-set.
Methods of Cross Validation
Validation
In this method, we perform training on 50% of the given data-set and the rest 50% is used for testing purposes. The major drawback of this method is that we perform training on 50% of the dataset, it may possible that the remaining 50% of the data contains some important information which we are leaving while training our model i.e higher bias.
LOOCV (Leave One Out Cross Validation)
In this method, we perform training on the whole data-set but leaves only one data-point of the available data-set and then iterates for each data-point. It has some advantages as well as disadvantages also.
An advantage of using this method is that we make use of all data points and hence it is low bias.
The major drawback of this method is that it leads to higher variation in the testing model as we are testing against one data point. If the data point is an outlier it can lead to the higher variation. Another drawback is it takes a lot of execution time as it iterates over ‘the number of data points’ times.
K-Fold Cross Validation
In this method, we split the data-set into k number of subsets(known as folds) then we perform training on all the subsets but leave one(k-1) subset for the evaluation of the trained model. In this method, we iterate k times with a different subset reserved for the testing purposes each time.
In machine learning, we couldn’t fit the model on the training data and can’t say that the model will work accurately for the real data. For this, we must assure that our model got the correct patterns from the data, and it is not getting up too much noise. For this purpose, we use the cross-validation technique.
Cross-validation is a technique in which we train our model using the subset of the data-set and then evaluate using the complementary subset of the data-set.
The three steps involved in cross-validation are as follows :
Reserve some portion of the sample data-set.
Using the rest data-set train the model.
Test the model using the reserve portion of the data-set.
Methods of Cross Validation
Validation
In this method, we perform training on 50% of the given data-set and the rest 50% is used for testing purposes. The major drawback of this method is that we perform training on 50% of the dataset, it may possible that the remaining 50% of the data contains some important information which we are leaving while training our model i.e higher bias.
LOOCV (Leave One Out Cross Validation)
In this method, we perform training on the whole data-set but leaves only one data-point of the available data-set and then iterates for each data-point. It has some advantages as well as disadvantages also.
An advantage of using this method is that we make use of all data points and hence it is low bias.
The major drawback of this method is that it leads to higher variation in the testing model as we are testing against one data point. If the data point is an outlier it can lead to the higher variation. Another drawback is it takes a lot of execution time as it iterates over ‘the number of data points’ times.
K-Fold Cross Validation
In this method, we split the data-set into k number of subsets(known as folds) then we perform training on all the subsets but leave one(k-1) subset for the evaluation of the trained model. In this method, we iterate k times with a different subset reserved for the testing purposes each time.
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