Create confusion matrices and compute metrics with Python
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Understand how to create confusion matrices and compute essential model metrics in Python. Learn to evaluate classification models with metrics like accuracy, precision, recall, F1-score, and ROC-AUC to gauge their performance.
At a Glance
Confusion matrices are a common and useful technique for classification tasks. They provide a perspective on the accuracy and effectiveness of algorithms. In this project, work with confusion matrices and classification accuracy as we analyze the effectiveness of spam detection. Uncover valuable insights into sensitivity, specificity, accuracy, and precision. Join us on a journey of discovery through the matrix of classification metrics for some effective spam detection.
This hands-on project is based on the Create a confusion matrix with Python tutorial. The guided project format combines the instructions of the tutorial with the environment to execute these instructions without the need to download, install, and configure tools.
A look at the project ahead
- Use Python to create confusion matrices
- Learn to derive different measures from a confusion matrix mathematically
- Derive measured from a confusion matrix with sklearn
What you’ll need
- No installation required: Everything is available in the JupyterLab, including any Python libraries and data sets.
- Basic understanding of Python: Some basic understanding of Python will be beneficial.
- Some understanding of statistical concepts: It’s helpful to have some understanding of statistic concepts, particularly terms like accuracy, specificity, and precision.
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