Gradient Boosting
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The scatter plot allows for a visual comparison between the actual values and the predicted values. It helps assess the performance and accuracy of the regression model by examining the closeness of the plotted points to a diagonal line.
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The points aligned closely to the diagonal, it indicates a strong correlation between the actual and predicted values, suggesting a good fit of the model.
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The plot shows that the training MSE line lies on the x-axis, indicating that the model achieves a perfect or near-perfect fit to the training data with very low error. This suggests that the model is able to accurately capture the patterns and relationships present in the training data.
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On the other hand, the validation MSE starts at around 4,000 for smaller training set sizes and gradually decreases as the training set size increases. This trend indicates that as more data is used for training, the model's performance improves on unseen data, resulting in lower error. The validation MSE reaching around 2,000 for larger training set sizes suggests that the model generalizes well and performs effectively on new and unseen data.
Overall, the learning curve demonstrates the relationship between training set size and model performance, highlighting the trade-off between underfitting and overfitting. It provides insights into how the model's accuracy improves with more training data and helps assess the model's ability to generalize beyond the training set.
Performance Metrics for Gradient Boosting Regression:
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Mean Squared Error (MSE): The MSE value is 12.556498997824992. It is a measure of the average squared difference between the actual values and the predicted values. A lower MSE indicates a better fit of the model to the data.
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Coefficient of Determination (R-squared): The R-squared value is 0.9982705831916286. It represents the proportion of the variance in the dependent variable (target variable) that can be explained by the independent variables (input features). A higher R-squared value indicates a better fit of the model, with a value of 1.0 indicating a perfect fit.