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Model Evaluation and Hyperparameter Tuning
- Updated on 10/09/2024
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Evaluating Your Model
Accuracy: The ratio of correctly predicted instances to the total instances.
Precision: The ratio of correctly predicted positive observations to the total predicted positives.
Recall: The ratio of correctly predicted positive observations to all the observations in the actual class.
F1 Score: The weighted average of Precision and Recall.
Confusion Matrix: A table used to evaluate the performance of a classification model.
Hyperparameter Tuning
What are Hyperparameters?Parameters set before training the model (e.g., learning rate, number of trees in a forest).
Grid Search:An exhaustive search over specified parameter values to find the best combination.
Random Search:Randomly selects parameter values from a given range and evaluates their performance.
Cross-Validation:A technique to evaluate model performance by dividing the data into multiple folds and training/testing the model on different folds.
Example
Evaluating a Classification Model
Import LibrariesImport necessary libraries to start the evaluation process.
Evaluate the ModelAssess the model's performance using evaluation metrics like accuracy, precision, recall, and F1 score.
Hyperparameter Tuning with Grid Search
Import LibrariesImport libraries needed for grid search and hyperparameter tuning.
Define Parameter Grid and Perform Grid SearchSet up the parameter grid and use grid search to find the optimal hyperparameters.
Activity
Choose a dataset and a classification model. Evaluate the model using classification_report and confusion_matrix. Perform hyperparameter tuning using Grid Search. Share your results and discuss the impact of different hyperparameters with a peer.
Quiz
1. What does the accuracy metric measure?
- 1) The ratio of correctly predicted instances to the total instances
- 2) The ratio of correctly predicted positive observations to the total predicted positives
- 3) The ratio of correctly predicted positive observations to all the observations in the actual class
- 4) The weighted average of Precision and Recall
2. True or False: Hyperparameters are learned from the data during training.
- 1) True
- 2) False
3. Which technique involves an exhaustive search over specified parameter values?
- 1) Grid Search
- 2) Random Search
- 3) Cross-Validation
- 4) Model Evaluation
4. What is the purpose of the Confusion Matrix?
- 1) To evaluate the performance of a classification model
- 2) To preprocess data
- 3) To train models
- 4) To visualize data
5. Which library provides functions for model evaluation in Python?
- 1) sklearn.metrics
- 2) pandas
- 3) numpy
- 4) matplotlib
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