In machine learning, creating a model is not the end goal. What really matters is how the model performs on new, real-world data. This is where model evaluation comes into play.
Let’s say a student learns only from the textbook and scores well. But if the exam has tricky or new questions, the student might struggle. Similarly, a model might do well on training data but fail in real situations. So, we need to test it the right way.
What Is Model Evaluation?
Model evaluation is the process of checking a machine learning model’s performance using data it hasn’t seen before. This helps us understand whether the model is useful or just memorising data.
Popular Evaluation Methods:
Confusion Matrix – For classification tasks; gives accuracy, precision, recall, and F1 score.
Cross-Validation – Tests the model on different data splits; helpful when data is less.
MAE and MSE – For regression problems; show how far predictions are from actual values.
ROC-AUC – Checks how well the model can tell between two classes.
Final Words
Don’t skip evaluation. It helps you find out if your model is reliable in real use. Choose the right metric based on your problem, and always test on fresh data. This way, your model won’t just look good on paper—it’ll work when it matters.
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