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Scikit Learn

Manufactured by Anaconda

Scikit-Learn 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. Scikit-Learn provides efficient and well-tested implementations of a variety of machine learning algorithms, making it a popular choice for researchers and practitioners in the field of data science and artificial intelligence.

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5 protocols using Scikit Learn

We used confusion matrices to compare the prediction of DL models with the reference standard (ACD < 2.4 mm). The matrices included the area under the receiver operating characteristic curve (AUC) of the receiver operating characteristic (ROC) curves, accuracy, sensitivity, and specificity. The ROC curve was plotted by applying different thresholds to the output score maps from the DL model. The closer the AUC is to 1, the better the DL model. Accuracy, sensitivity, and specificity are expressed as follows:
Accuracy=TP+TNTP+TN+FN+FP, Sensitivity=TPTP+FN, Specificity=TNTN+FP,
TP, TN, FP, and FN represent true positive, true negative, false positive, and false negative, respectively. Python (version 3.7) and Scikit_learn modules (Anaconda, version 1.9.12, Continuum Analytics) were used to perform the statistical analysis.
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The evaluation matrices used to assess the performance of the semi-supervised GANs and the supervised DL models included accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals (95% CIs). The accuracy, sensitivity, and specificity of the DL algorithms for detecting closed angles were computed according to the reference standard. All statistical analyses were performed using the Python (version 3.7) and Scikit_learn modules (Anaconda, version 1.9.12, Continuum Analytics)
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The codes used in our work are based on standard, publicly available software packages. Pre- and post-processing data and the generation of figures were performed using the Anaconda (v4.10) packages Matplotlib v3.4, Scikit Learn v1.0, Scipy v1.5 and NetworkX v2.3. Figures were then assembled using Adobe Illustrator. Chimera (v1.13)69 (link) was used for visualization of the 3D structures generated.
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The analysis was performed in an Anaconda Python environment with the following packages: Python (version 3.9), BioPython (version 1.79), Matplotlib (version 3.5.1), Plotly (version 4.14.3), Scikit-learn (version 1.0.2), Pandas (version 1.4.1), Pyteomics (version 4.5.3), Matplotlib-venn (version 0.11.6), Seaborn (version 0.11.2), UMAP-learn (version 0.5.2), HDBSCAN (version 0.8.28), and xlrd (version 2.0.1), Logomaker v0.8. An R environment within Anaconda was also used, consisting of R-essentials (version 4.1), R-base (version 4.1.2), and R-devtools (version 2.4.3). These tools and environments enabled data processing and creation of visualizations.
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The Anaconda platform (Anaconda Software Distribution. Computer software. Vers. 2-2.4.0. Anaconda, Nov. 2016) with the Python package scikit-learn (version 0.23.2) was used to classify fissures and pain from the MRI markers (Fig. 1). In specific, the "Random forest" machine-learning algorithm with 100 trees was used. For development of the models, the machine-learning algorithm was trained on 75% of the data. To provide insight into the different models and determine the usefulness of each MRI maker at predicting the fissure categories and pain-responses, importance scores that displayed the relative contribution of each marker to the model were determined as relative values with highest score 1.0. The trained models were then tested on the remaining 25% unseen data to validate their diagnostic performance in terms of accuracy (proportion of true classifications out of all classifications), precision (proportion of true positive classifications out of all positive classifications), recall (proportion of true positive classifications out of all positive events), f1-score (harmonic mean of the model's precision and recall). The procedure was repeated 10 times and the results were averaged. Finally, learning curves for the train and test data were plotted to evaluate the quality of the model and possible under/overfitting.
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