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Learn AI with Python · Lesson

Evaluating Model Performance

Accuracy, F1 score, and error metrics.

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Evaluating Model Performance

Evaluating the performance of a machine learning model is crucial to ensure its effectiveness. We use metrics like accuracy, precision, recall, and F1 score for classification problems.

Evaluating Model Performance — illustration 1

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Accuracy

Accuracy measures the ratio of correctly predicted instances to the total number of instances:

Accuracy = (True Positives + True Negatives) / Total Instances

While useful, accuracy alone might not be sufficient for imbalanced datasets.

All lessons in this course

  1. The Concept of Linear Regression
  2. Implementing Linear Regression in Python
  3. The Concept of Logistic Regression
  4. Logistic Regression Implementation
  5. Evaluating Model Performance
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