TopicHow 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. SourceCogito offers a complete data collection and processing service for training data as a service for AI and Machine learning based services. It is providing wide range of services like Visual Search, Image Annotation, Machine Learning, Sentiment Analysis, Data Collection, Data Classification, Search Relevance, healthcare training data, contact center services, Content Moderation, Audio Transcription, Video Transcription and OCR Transcription services with high quality and accuracy.