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Scikit learn 0

Manufactured by Anaconda
Sourced in United States

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.

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3 protocols using scikit learn 0

1

Hyperspectral Imaging Analysis Workflow

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The hyperspectral images were firstly preprocessed in ENVI 4.7 (ITT Visual Information Solutions, Boulder, CO, USA) to define the area of the samples. Matlab R2019b (The Math Works, Natick, MA, USA) is a powerful mathematical calculation software. Pixel-wise spectra extraction, pixel-wise PCA and object-wise PCA were conducted in Matlab R2019b. SVM, LR, and the corresponding grid-searches were undertaken in the scikit-learn 0.23.1 (Anaconda, Austin, TX, USA) using python 3.1. CNN architecture was built in MXNet1.4.0 (MXNetAmazon, Seattle, WA, USA). Least significant difference tests were conducted in SPSS V19.0 software (The SPSS Inc., Chicago, IL, USA).
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2

Multivariate Analytical Modeling Techniques

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Outlier detection was conducted in MATLAB R2015b (The MathWorks, Natick, MA, USA). SNV, MSC, and detrending were performed in the Unscrambler X 10.1 (Camo AS, Oslo, Norway). FD was undertaken in MATLAB R2015b (The MathWorks, Natick, MA, USA). For the model establishment, the construction of the PLS model was performed in the Unscrambler X 10.1 (Camo AS, Oslo, Norway). SVR was carried out in the scikit-learn 0.23.1 (Anaconda, Austin, TX, USA) using python 3.1. The CNN model and fine-tuning were conducted in MXNet1.4.0 (MXNetAmazon, Seattle, WA, USA).
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.
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3

Evaluating Machine Learning Models' Performance

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For model establishment, PLS-DA and PLSR were performed in R2019b (The MathWorks, Natick, MA, USA). SVC, and SVR were conducted in the scikit-learn 0.23.1 (Anaconda, Austin, TX, USA) using python 3.1. The CNN models were conducted in MXNet 1.4.0 (MXNetAmazon, Seattle, WA, USA). For feature selection, RF was performed in R2019b (The MathWorks, Natick, MA, USA). WPLS was carried out in the Unscrambler X 10.1 (Camo AS, Oslo, Norway). Saliency map was conducted in MXNet1.4.0 (MXNetAmazon, Seattle, WA, USA).
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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