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Importance of Model Evaluation
Model evaluation is the process of applying various metrics to assess a machine learning model’s performance.
AI model improves overtime with constructive feedback. This is an iterative process where you build the model,
evaluate its performance using appropriate metrics, refine it based on the feedback, and repeat until the desired
accuracy is achieved. It’s similar to tuning a musical instrument—regularly checking its sound quality, making
adjustments, and fine-tuning until the melody is harmonious and meets the desired standard.
Some of the advantages of evaluating a model are as follows:
• Evaluation ensures that the model is operating correctly and optimally.
• Evaluation is an initiative to understand how well it achieves its goals.
• Evaluation helps to determine what works well and what could be improved in a program.
Human
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Learning
subject test result better results
Process
Machine Training the Testing the Fine tuning the
Evaluating the
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Process training data. testing data performance
Need for Model Evaluation
Model evaluation is like giving the AI model a performance review. It helps identify its strengths, pinpoint
weaknesses, and determine how well it fits the task at hand. This feedback acts as a guide to refine and improve
the model, making it more reliable and trustworthy. The process is continuous, just like fine-tuning a skill to
achieve the best results. Depending upon the type and the purpose of the evaluation model, there are different
types of evaluation techniques, like Train-Test split, Confusion Matrix, etc.
Splitting the Training Set Data for Evaluation
Splitting the training set data is a crucial step in model evaluation, allowing for a systematic assessment of the
model’s performance by creating distinct datasets for training, validation, and testing. Let’s learn more about
splitting the training set using the Train-Test split in detail.
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