Accuracy & Precision of a Machine Learning Model? #DataScience #InterviewQuestions

Analytics Vidhya · Beginner ·📊 Data Analytics & Business Intelligence ·3y ago

Key Takeaways

The video explains the difference between accuracy and precision of a machine learning model, highlighting how accuracy measures the closeness of predicted values to target values, while precision measures the consistency of predicted values across multiple runs.

Full Transcript

Prashant what is the difference between accuracy and precision of a machine learning model accuracy of a machine learning model is basically how close are the predicted values from your to the Target values simple in the case of precision what we are measuring is how close are the predicted values to themselves let me give you a simple example let's say for a given test data point the predicted value is 100 from the machine learning model and then you retrain the model now the predicted value for the same test data point is 102 the third time the predicted value for the same test data point is 98 so you see every time the predicted values are changing so how close are these predicted values to themselves that defines the Precision of a model yeah now I'm clear on the two terms thanks

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This video teaches the difference between accuracy and precision in machine learning models, providing a clear understanding of how to evaluate model performance. It is essential for data scientists and machine learning engineers to understand these concepts to build and improve their models.

Key Takeaways
  1. Define accuracy and precision in machine learning
  2. Understand how accuracy measures closeness to target values
  3. Understand how precision measures consistency of predicted values
  4. Evaluate model performance using accuracy and precision metrics
💡 Precision measures the consistency of predicted values across multiple runs, which is crucial for building reliable machine learning models.

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