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 used in machine learning to evaluate the performance of a model on unseen data. It involves dividing the available data into multiple folds or subsets, using one of these folds as a validation set, and training the model on the remaining folds. This process is repeated multiple times, each time using a different fold as the validation set. Finally, the results from each validation step are averaged to produce a more robust estimate of the model’s performance.
The main purpose of cross validation is to prevent overfitting, which occurs when a model is trained too well on the training data and performs poorly on new, unseen data. By evaluating the model on multiple validation sets, cross validation provides a more realistic estimate of the model’s generalization performance, i.e., its ability to perform well on new, unseen data.
There are several types of cross validation techniques, including k-fold cross validation, leave-one-out cross validation, and stratified cross validation. The choice of technique depends on the size and nature of the data, as well as the specific requirements of the modeling problem.
In summary, cross validation is an important step in the machine learning process and helps to ensure that the model selected for deployment is robust and generalizes well to new data.
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 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 the 50% of the given data-set and rest 50% is used for the testing purpose. The major drawback of this method is that we perform training on the 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 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 the 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 testing purpose each time.
It is always suggested that the value of k should be 10 as the lower value
of k is takes towards validation and higher value of k leads to LOOCV method.
Example The diagram below shows an example of the training subsets and evaluation subsets generated in k-fold cross-validation. Here, we have total 25 instances. In first iteration we use the first 20 percent of data for evaluation, and the remaining 80 percent for training([1-5] testing and [5-25] training) while in the second iteration we use the second subset of 20 percent for evaluation, and the remaining three subsets of the data for training([5-10] testing and [1-5 and 10-25] training), and so on.
Total instances: 25
Value of k : 5
No. Iteration Training set observations Testing set observations
1 [ 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24] [0 1 2 3 4]
2 [ 0 1 2 3 4 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24] [5 6 7 8 9]
3 [ 0 1 2 3 4 5 6 7 8 9 15 16 17 18 19 20 21 22 23 24] [10 11 12 13 14]
4 [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 20 21 22 23 24] [15 16 17 18 19]
5 [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19] [20 21 22 23 24]
Comparison of train/test split to cross-validation
Advantages of train/test split:
- This runs K times faster than Leave One Out cross-validation because K-fold cross-validation repeats the train/test split K-times.
- Simpler to examine the detailed results of the testing process.
Advantages of cross-validation:
- More accurate estimate of out-of-sample accuracy.
- More “efficient” use of data as every observation is used for both training and testing.
Advantages of Cross Validation:
- Overcoming Overfitting: Cross validation helps to prevent overfitting by providing a more robust estimate of the model’s performance on unseen data.
- Model Selection: Cross validation can be used to compare different models and select the one that performs the best on average.
- Hyperparameter tuning: Cross validation can be used to optimize the hyperparameters of a model, such as the regularization parameter, by selecting the values that result in the best performance on the validation set.
- Data Efficient: Cross validation allows the use of all the available data for both training and validation, making it a more data-efficient method compared to traditional validation techniques.
Disadvantages of Cross Validation:
- Computationally Expensive: Cross validation can be computationally expensive, especially when the number of folds is large or when the model is complex and requires a long time to train.
- Time-Consuming: Cross validation can be time-consuming, especially when there are many hyperparameters to tune or when multiple models need to be compared.
- Bias-Variance Tradeoff: The choice of the number of folds in cross validation can impact the bias-variance tradeoff, i.e., too few folds may result in high variance, while too many folds may result in high bias.
Python code for k fold cross-validation.
“An Introduction to Statistical Learning” by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
“Pattern Recognition and Machine Learning” by Christopher Bishop
“Machine Learning” by Tom Mitchell
“Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron
These books provide a comprehensive introduction to machine learning and cover cross validation in detail. They are a great resource for those looking to gain a deeper understanding of the topic.
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