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.
Scikit Learn
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.
Lab products found in correlation
5 protocols using Scikit Learn
Evaluating Deep Learning Models' Accuracy
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.
Evaluating Semi-Supervised GANs and DL Models
Software Tools for Data Analysis and Visualization
Comprehensive Bioinformatics Analysis Pipeline
MRI-Based Fissure and Pain Prediction
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