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Model Validation in Machine Learning
Model validation is a technique where we try to validate the model that has been built by gathering, preprocessing, and feeding appropriate data to the machine learning algorithms. We can not directly feed the data to the model, train it and deploy it. It is essential to validate the performance or results of a model to check whether a model is performing as per our expectations or not. There are multiple model validation techniques that are used to evaluate and validate the model according to the different types of model and their behaviors.
In this article, we will discuss model validation, the core idea behind that, why it is needed, the advantages of the same, and how one can validate the model using SKLearn. This will help one to understand the concept of model validation more profoundly, and one will be able to validate the model using SKLearn.
What is Model Validation?
Machine learning is all about the data, its quality, quantity, and playing with the same. Here most of the time, we collect the data; we have to clean it, preprocess it, and then we have to apply the appropriate algorithm and get the best-fit model out of it. But after getting a model, the task is not done; the model validation is as important as the training.
Directly training and then deploying a model would not work, and in sensitive areas like the healthcare model, there is a huge amount of risk associated, and real-life predictions have to be made; in this case, there should not be an error in the model as it can cost a lot then.
Advantages of Model Validation
Here are many advantages that model validation provides.
Quality of the Model
The first and foremost advantage of model validation is the quality of the model; yes, we can quickly get an idea bout the performance and quality of the model by validating the same.
The flexibility of the Model
Secondly, validating the model makes it easy to get an idea about the flexibility. Model validation helps make the model more flexible also.
Overfitting and Underfitting
Model validation help identify if the model is underfitted or overfitted. In the case of underfitting, the model gives high accuracy in training data, and the model performs poorly during the validation phase. In the case of underfitting, the model does not perform well during either the training or validation phase.
There are many techniques available for validating the model; let us try to discuss them one by one.
Train Test Split
Train test split is one of the most basic and easy model validation techniques used to validate the data. Here we can easily split the data into two parts, the training set, and the testing set. Also, we can choose in which ratio we want to split the data.
There is one problem associated with the train_test_split method if there is any subset of the data that is not present in the training set and is present in the testing set, then the model will give an error.
Hold Out Approach
Hold out approach is also very similar to the train test split method; just here, we have an additional split of the data. While using the train test split method, it may happen that there are two splits of the data, and the data can be leaked, due to which the overfitting of the model can take place. To overcome this issue, we can still split the data into one more part called hold out or validation split.
So basically, here, we train our data on the big training set and then test the model on the testing set. Once the model performs well on both the training and testing set, we try the model on the final validation split to get an idea about the behavior of the model in unknown datasets.
K Fold Cross Validation
K fold cross-validation is one of the widely used and most accurate methods for splitting the data into its training and testing points. In this approach, the logic or the working mechanism of the KNN algorithm is used. Same as the KNN algorithm, here we also have a term called K which is the number of splits of the data.
In this method, instead of splitting the data a single time, we split the data multiplied based on the value of K. Let us suppose that the value of K is defined as 5. Then the model will split the dataset five times and will choose different training and testing sets every single time.
By doing we get a significant advantage in that the model can test on all the data, and the model would not be biased.
Lean One Out Method
Leave one out is also a variant of the K fold cross-validation technique where we have defines the K as n. Where n is the number of samples or data observations we have in our dataset. Here the model trains and tests on every data sample, and the model considers each sample as a testing set and others as a training set.
Although this method is not used widely, the holdout and K fold approach solves most of the issues related to model validation.
Model validation is one of the most important tasks in machine learning which should be implemented for all models to be deployed.
Model validation gives us an idea about the behavior of the model, its performance on the data, problems like overfitting and underfitting, and the errors associated with the model.
Train test split and hold-out approaches are easy and the most common method for model validation where the data is splintered into two or three parts and the model is validated on the testing set.
The k-fold approach is a more advanced and accurate method for model validation whew the data is splitted K times and every time the training and testing set keep varying.
Leave one out is a variant of the K fold approach where the model leaves one observation of the data out of the training set and use it as a testing set.
In this article we discussed model validation, its importance, the advantages of model validation, and the different model for validating the machine learning model. This will help one to understand the model validation and apply it to any data and any model.
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