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    Precision is a metric used to evaluate the performance of a binary classification model. It is the ratio of true positives (TP) to the total number of positive predictions (TP + FP).

    In other words, precision measures the accuracy of positive predictions made by the model. It answers the question, “Out of all the positive predictions made by the model, how many were actually correct?”

    The formula for precision is:

    Precision = TP / (TP + FP)

    A high precision score means that the model makes few false positive predictions and is good at identifying positive instances. However, a high precision score alone does not necessarily mean that the model is performing well. It could be that the model is missing a large number of positive instances, leading to low recall.

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    Dr. Ujjal Marjit leads the Centre for Information Resource Management of the University of Kalyani, India. He received his bachelor honours degree from Visva Bharati, Central University and Master in Computer Application from Jadavpur University, India.He did his BLISc and MLISc from Madurai Kamraj University, India. He obtained his PhD in Computer Science and Engineering from University of Kalyani. He was also a visiting researcher at Norwegian University of Science and Technology (NTNU), Norway. Dr. Marjit was a member of the Association for Computing Machinery (ACM), USA. He has coauthored several book chapters and over 70 research publications in various International Journals and Conferences. Dr. Marjit attended many national and international conferences in India and abroad ( Germany, London, Finland, Norway, Netherlands). He has been working in University since 2001.