Machine Learning Notes (6) Classification Evaluation Index, Confusion Matrix, Precision Rate, Recall Rate, PR Curve, ROC Curve - Code World

Machine Learning Notes (6) Classification Evaluation Index, Confusion Matrix, Precision Rate, Recall Rate, PR Curve, ROC Curve

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Machine Learning Notes (6) Classification Evaluation Index, Confusion Matrix, Precision Rate, Recall Rate, PR Curve, ROC Curve

sklearn classifier evaluation indicators (precision rate, confusion matrix, precious-recall-Fmeasur, ROC curve, loss function)

Machine Learning common types of evaluation: accuracy rate (Precision), recall (Recall), F value (F-measure), ROC curve, AUC, accuracy (Accuracy)

pytorch advanced learning (7): drawing and code of confusion matrix, recall rate, precision rate, ROC curve and other indicators in the process of neural network model verification

ROC classification confusion matrix and assessment indicators of evaluation index] [/ AUC curve

[Summary of model evaluation indicators in deep learning (confusion matrix, recall, precision, F1, AUC area, ROC curve, ErrorRate)]

PytorchCNN project construction 7 --- deep learning model evaluation index Accuracy, Precision, Recall, ROC curve

Evaluation indicators for image classification and image segmentation (confusion matrix accuracy rate precision recall rate F1 score IOU dice coefficient)

Machine learning performance evaluation indicators: ROC curve and PR curve

Sklearn.metrics evaluation method described in (accuracy_score, recall_score, roc_curve, roc_auc_score, confusion_matrix, classification_report)

Evaluation indicators of machine learning model pros and cons: confusion matrix, PR curve and average accuracy (with code implementation)

Machine learning multi-classification template AUC AP PR curve confusion matrix algorithm comparison

Confusion matrix and ROC curve for binary classification and multi-classification

Target detection evaluation index mAP: from Precision, Recall, PR curve to AP

Model evaluation index—ROC curve

ROC and AUC calculation, sklearn.metrics roc_curve, precision_recall_curve, auc, roc_auc_score, multi-label classification ROC curve drawing

Classification evaluation indicators in simple machine learning evaluation indicators (acc, recall, precision, F1, fusion matrix,

[Machine Learning Practice] ROC Curve Drawing for Classification Algorithm Evaluation (Multiple Model Comparison)

5.7 --- computational cost increased cost-sensitive classification ROC curve of FIG response rate - the rate accuracy

Classification indicators: accuracy rate, precision rate, recall rate, F1 score and ROC, AUC, macro average, weighted average

python confusion matrix (confusion_matrix) FP, FN, TP, TN, accuracy rate (Precision), recall (Recall), accuracy (Accuracy) detailed

Common evaluation indicators for machine learning: ACC, AUC, ROC curve

Accuracy, precision, recall, true rate, false positive rate, ROC/AUC

Deep learning theory - Python import library and function, two evaluation indicators (Top-5 error rate and Top-1 error rate), ROC curve and AUC concept

Recall (Recall), precision (Precision), sensitivity (Sensitivity), specificity (Specificity), F1, PR curve, ROC, AUC application scenarios

Detailed explanation of concepts related to machine vision performance indicators - confusion matrix, IoU, ROC curve, mAP, etc.

Examples [Notes machine learning] plotted ROC curve the Python

Machine learning notes, use metrics.classification_report to display precision, recall, f1 index

Classification indicators of data mining: recall rate, precision, accuracy rate, false alarm rate and missed alarm rate

PR curve and ROC curve drawing

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