PYTHON Tutorial

Evaluation and Selection

Steps Involved

  • Define Evaluation Metrics: Specify relevant metrics (e.g., accuracy, precision, recall) to assess model performance.
  • Split Data: Divide the dataset into training and testing sets to prevent overfitting.
  • Train Models: Develop and train multiple candidate models using different algorithms or hyperparameters.
  • Evaluate Models: Use the testing set to compute evaluation metrics for each model.
  • Cross-Validation: Repeat evaluation process with different data splits to reduce bias.
  • Select Best Model: Choose the model with the best performance based on evaluation results.

Key Concepts

  • Model Evaluation: Assessing model performance on unseen data.
  • Cross-Validation: Splitting the data into multiple subsets for training and testing to reduce bias.
  • Confusion Matrix: Tabulating actual and predicted labels to visualize model performance.
  • ROC Curve: Graphing the true positive rate vs. false positive rate at various thresholds.
  • Precision: True positives / (true positives + false positives).
  • Recall: True positives / (true positives + false negatives).

Example

Consider a dataset with three classes: healthy, sick, and unknown.

  • Evaluate Models: Train logistic regression, decision tree, and SVM models and compute accuracy, precision, and recall for each class.
  • Cross-Validation: Perform 10-fold cross-validation to obtain more robust results.
  • Select Best Model: Choose the model with the highest overall accuracy, balanced precision, and recall for all classes.

Accessibility Guide

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  • Define technical terms.
  • Provide examples and visuals.
  • Use interactive tools or online platforms for hands-on practice.
  • Provide resources for further learning.
  • Ensure that the guide is accessible to diverse learners, including those with disabilities or language barriers.