TopicHow to Evaluate Machine Learning Model Performance Without Labeled Data?

  • Wed 11th Sep 2019 - 11:31am

    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. Source

    Cogito offers high-quality human annotated quality dataset for artificial intelligence and machine learning with more accuracy to make visual search, image search, sentiment analysis and data collection or classification and transcription service more useful and productive.

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