How to Evaluate Machine Learning Model Performance Without Labeled Data?
Evaluating the machine learning model is very important to check the accuracy level and make sure this model will work well in real-life use. Evaluation means, checking the prediction of model after giving a raw data to recognize the data or object learn from previous machine learning training process.
What is Labeled Data for Machine Learning?
Hence, you need certain data to evaluate the model accuracy. In case of labeled data images are annotated for computer vision to recognize the objects to training the machines or use such data while evaluating the model prediction.
Labeled data help machines to learn certain patterns and recognize the similar objects when shown in real-life use. And for evaluating the ML model you again the labeled data to compare if the model is making the right prediction or not.
And there are several methods to evaluate the ML model performance. And in each evaluation process, training data is shown to model for recognizing the object. Labeled helps the ML model for making the prediction at faster speed.
How to Evaluate a ML Model Without Labeled Data?
However, if you don’t have labeled data for machine learning how will you evaluate the ML model. The model validation techniques like Holdout, Cross-Validation, Leave-One-Out, Random Subsampling Validation and Bootstrapping ML Validation Method.
And to evaluate the machine learning model without labeled data is a difficult, as how will you compare the labeled data with unannotated data, hence you need help experts to evaluate the model accurately and make it work with quality results.
Actually, ML model validation is the right technique to evaluate such models. And only experts can help you to evaluate the model without using the labeled data. Cogito is the company providing the data labeling and model validation service for AI and machine learning models. It is providing the training data for model validation with highly-skilled ML engineers helping AI developers to build the right model. Source