Ai Model Evaluation — Free AI & ML Tutorial
Learn Ai Model Evaluation in AI & ML with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn Ai Model Evaluation in AI & ML with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
Written & reviewed by the Syllab.in Academic Team (CBSE/NCERT subject experts) · Updated
Ai Model Evaluation in AI & ML
Building an ML model is only half the job. You must evaluate how well it performs on NEW, unseen data. A model that memorises training data (overfitting) will fail on real-world data.
Train/Test Split: Always split your dataset into training data (used to train the model, typically 80%) and test data (used to evaluate, typically 20%). The model never sees test data during training.
Classification metrics: Accuracy (% of correct predictions), Precision (of all positive predictions, how many were correct?), Recall (of all actual positives, how many did we find?), F1 Score (balance of precision and recall). For spam detection: recall is critical — missing a spam email is bad.
Overfitting vs Underfitting: Overfitting = model memorises training data, fails on new data (high train accuracy, low test accuracy). Underfitting = model too simple, performs poorly everywhere. Goal: find the "Goldilocks zone" — just right.
Ai Model Evaluation — Syntax
# Confusion Matrix (for classification): # Predicted: YES Predicted: NO # Actual: YES True Positive False Negative # Actual: NO False Positive True Negative # # Accuracy = (TP + TN) / Total # Precision = TP / (TP + FP) # Recall = TP / (TP + FN) # F1 Score = 2 * Precision
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