Welcome to the model evaluation tutorials section! Here, you will find various resources to understand how to evaluate the performance of machine learning models. Let's dive in!
Introduction to Model Evaluation
Model evaluation is a critical step in the machine learning process. It helps us understand how well our models are performing and where they might need improvement. Below are some key concepts and techniques in model evaluation.
Key Concepts
- Accuracy: The ratio of correctly predicted observations to the total observations.
- Precision: The ratio of correctly predicted positive observations to the total predicted positive observations.
- Recall: The ratio of correctly predicted positive observations to the all observations in actual class.
- F1 Score: The weighted average of Precision and Recall, and is a better measure than either of them separately.
Common Evaluation Metrics
- Mean Absolute Error (MAE): Measures the average magnitude of the errors in a set of predictions, without considering their direction.
- Mean Squared Error (MSE): Measures the average squared difference between the estimated values and the actual value.
- Root Mean Squared Error (RMSE): The square root of the mean squared error.
Practical Examples
Let's look at some practical examples of model evaluation in different scenarios.
Example 1: Classification Model
Imagine you have a classification model that predicts whether an email is spam or not. To evaluate this model, you can use metrics like accuracy, precision, recall, and F1 score.
Example 2: Regression Model
Suppose you have a regression model that predicts housing prices. In this case, you can use MAE, MSE, and RMSE to evaluate the model's performance.
Resources
For more in-depth learning, we recommend the following resources:
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