AI — Model Evaluation
Evaluation Metrics
| Metric | Use Case |
|---|---|
| Accuracy | Classification |
| Precision | Positive predictions |
| Recall | Actual positives |
| F1 Score | Balance |
| AUC-ROC | Binary classification |
Classification Metrics
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
accuracy = accuracy_score(y_true, y_pred)
precision = precision_score(y_true, y_pred)
recall = recall_score(y_true, y_pred)
f1 = f1_score(y_true, y_pred)
Regression Metrics
from sklearn.metrics import mean_squared_error, r2_score
mse = mean_squared_error(y_true, y_pred)
r2 = r2_score(y_true, y_pred)
Cross-Validation
from sklearn.model_selection import cross_val_score
scores = cross_val_score(model, X, y, cv=5)
Mini Practice
- Calculate metrics
- Use cross-validation
- Compare models
- Tune hyperparameters
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Related Topics
Frequently Asked Questions about Model Evaluation
What is Model Evaluation in AI?
Model Evaluation is a fundamental concept in AI. This lesson explains it step by step with clear examples, making it easy for beginners to understand.
How do I learn Model Evaluation?
Start by reading the explanation above, then try the code examples. Practice by modifying the examples and experimenting with different values. Hands-on practice is the best way to learn Model Evaluation.
Why is Model Evaluation important in AI?
Model Evaluation is essential for AI development. Understanding this concept will help you write better code and solve real-world problems more effectively.