Scikit learn 0
Scikit-learn 0.23.1 is a machine learning library for the Python programming language. It features various classification, regression, and clustering algorithms, including support vector machines, random forests, gradient boosting, k-means, and more. The library is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.
Lab products found in correlation
3 protocols using scikit learn 0
Hyperspectral Imaging Analysis Workflow
Multivariate Analytical Modeling Techniques
The coefficients of determination (R2) and root mean square error (RMSE) of calibration, validation and prediction set were calculated to evaluate model performance. The R2 of a robust model should approach 1, while the RMSE is close to 0.
Evaluating Machine Learning Models' Performance
It is critical to evaluate the model performance with appropriate indicators. Classification accuracy is used for assessing the qualitative analysis models. Classification accuracy is calculated as the ratio of correctly classified samples to the total number of samples. The closer it is to 100%, the better the model’s performance. The coefficients of determination (R2) and root mean square error (RMSE) of calibration, validation, and prediction set were applied to assess the performance of quantitative analysis models. The closer R2 of the model is to 1, the closer RMSE is to 0, indicating that the model performance is more satisfactory.
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