Machine learning is the automatic learning of computer algorithms that you can improve by using data and experience. Artificial intelligence (AI) stimulates human behavior with the help of technology or a mechanism that enables a machine to do so. Machine learning is a variant of AI which allows a machineto learn from past data without explicit programming automatically.
Cross-Validation And Its Role In Machine Learning
In machine learning, we can’t say that our model will work perfectly for accurate data, and therefore we couldn’t directly use the model on the data. To resolve this issue, we must ensure that our model gives accurate patterns from the data. For this issue, we will do cross-validation by using techniques.
The comparison and evaluation of learning algorithms by a statistical method is made by distributing the given data into two segments. One is used to learn or train a model, and the other is used to validate the model, known as cross-validation.
Methods of Cross-Validation
Holdout Method
This method will divide the data into a ‘training set’ and ‘test set.’ The training set is used to train, and the rest of the dataset is used to know how well the model affects unseen data.
K-Fold Cross-Validation
In this method, we will distribute the provided data into k sets. K = number of groups. After that holdout method is continued k times, as one part is treated as the test set and the rest are used as the training set. The training set is used for training, and the balanced dataset is figured out to get the performance. This process will be repeated till each group has been tested. You can check out the UT AUSTIN AI and Machine Learning online course to understand these concepts in detail.
Stratified K-Fold Cross-Validation
There is a large imbalance in the response variables in some cases. Then for such problems, a little variation in the K Fold cross-validation technique will take place, such that each fold considers the almost same percentage of samples of each target group the complete set. And in the case of prediction problems, the mean response value is almost uniform in all the folds.
Leave-P-Out Cross-Validation
In this method, we will treat p-observation as validation data, and if there are n data points, then the remaining data n-p is used to train the model. This can be applied and continued for all sets to cut the original sample on a validation set of p observations and a training set.
Which AI online course to choose?
As discussed above, you can choose AI online courses that offer you a complete learning experience depending on your knowledge. Now that you have seen above how cross-validation plays a significant role in AI and machine learning, you can jump on to advanced topics like machine learning, AI, etc. This will involve using various algorithms based on your knowledge, so it should be relatively easy for you to pick up your skills here.
References
https://www.javatpoint.com/cross-validation-in-machine-learning
