What is the Difference Between Evaluation and Validation in Machine Learning?


While on the other hand, evaluation in machine learning refers to assessment or test of entire machine learning model and its performance in various circumstances.

Machine Learning (ML) is the process of training a model using the training data sets with right algorithms for accurate predictions. Validation in machine learning is like a authorization or authentication of the prediction done by a trained model.

While on the other hand, evaluation in machine learning refers to assessment or test of entire machine learning model and its performance in various circumstances. It involves assessment of machine learning model training process, deep learning algorithms performance and how accurate is the predictions given in different situations.

How Evaluation is Different from Validation in ML?

Actually, validation in machine learning data relates to confirmation of datasets used in training the model, as well as the outputs given by the model. Means the model prediction data that again used to train the machines are validated manually by the experienced professionals resulting machine learns what is wrong in the prediction and improves the results.

ML Model Validation

For an example, annotated images are used to trained a model using the ML algorithms, and when these annotated images helps machine to learn certain patterns to detect the object or identify the object of interest when used in real-life.

When similar unannotated images are shown to machines to recognize the similar attributes, prediction is made over there from the data learn in the past. Here, experienced professionals check the outputs and validate the same whether it is correct and if any correction required, they do the same and again feed into the model training to improve the accuracy.

ML Model Evaluation

Conversely, evaluation in machine learning is different from validation. Actually, after model development, apart from accurate prediction, other aspects need to be evaluated for final testing. Checking the performance of ML model in different situations and what kind of training data or algorithms are used while developing such models involves there.

You can say it is kind of testing that mainly relates to check the performance of algorithm or whether it is suitable for your model training or not and what are the other parameters are also taken into account while performing the machine learning model training.

Methods Followed in Validation and Evaluation

Companies providing the ML model validation services follow various types of validation process in machine learning. Apart from several others methods, Cross-validation and manual validation are few popular ones followed by the professionals depending on the developers feasibility, algorithms compatibility and projects requirements.

While on the other hand, evaluation process in not specific, instead it is followed by the machine learning professionals as per their ease. However, certain fixed parameters rules there to ensure the industry standards making the ML models authentic and reliable. And evaluation process is basically done by humans manually.

Automated vs Manual Validation

Though, validation process could be more knotty as there are multiple validation methods, and choosing the right one could be challenging for ML professionals. Automated validation process can be performed using different types of validation methods. But human powered manual validation is the best one that can take little extra time but will help you to validate your models prediction accurately and improve the accuracy at best level.

Cogito is one the leading machine learning services company, offers machine learning model validation service and AI annotation QA service for such needs. It is providing human powered, model validation with team of dedicated professionals who are expert in creating and testing the training data for machine learning. It is also dedicatedly involved in image annotation services and renders an unbiased validation services for ML and AI models.