The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
\nCitation: Chicco, D., Jurman, G. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 21, 6 (2020). https://doi.org/10.1186/s12864-019-6413-7
\n\"To evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet. Accuracy and F1 score computed on confusion matrices have been (and still are) among the most popular adopted metrics in binary classification tasks. However, these statistical measures can dangerously show overoptimistic inflated results, especially on imbalanced datasets.\"
\nACCURACY, MCC, F1 SCORE.
\n\"An effective solution overcoming the class imbalance issue comes from the Matthews correlation coefficient (MCC), a special case of the ϕ phi coefficient. \"
\n\"Originally developed by Matthews in 1975 for comparison of chemical structures , MCC was re-proposed by Baldi and colleagues in 2000 as a standard performance metric for machine learning with a natural extension to the multiclass case.\"
\n\"The criterion of MCC is intuitive and straightforward: to get a high quality score, the classifier has to make correct predictions both on the majority of the negative cases, and on the majority of the positive cases, independently of their ratios in the overall dataset. F1 and accuracy, instead, generate reliable results only when applied to balanced datasets, and produce misleading results when applied to imbalanced cases. For these reasons, the authors suggested all the researchers working with confusion matrices to evaluate their binary classification predictions through the MCC, instead of using F1 score or accuracy.\"
\nhttps://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-019-6413-7
\n\"In statistics, the phi coefficient (or mean square contingency coefficient and denoted by φ or rφ) is a measure of association for two binary variables. In machine learning, it is known as the Matthews correlation coefficient (MCC) and used as a measure of the quality of binary (two-class) classifications, introduced by biochemist Brian W. Matthews in 1975.\"
\nhttps://en.wikipedia.org/wiki/Phi_coefficient
\n\"Sometimes in data science and machine learning we encounter problems of imbalanced classes. These are problems when one class might have more instances than another. This makes accuracy a bad metric. One metric that helps with this problem is Matthew’s Correlation Coefficient (MCC). \"
\nUSING the MCC in PYTHON and R
\n\"Using the MCC in python is very easy. You can just use Scikit Learn’s metrics API. The MCC can be executed through the function matthews_corrcoef. In R, you can use the function mcc from the mltools package.\"
\nhttps://thedatascientist.com/metrics-matthews-correlation-coefficient/
\nMCC = (TPTN – FPFN) / √(TP+FP)(TP+FN)(TN+FP)(TN+FN)
\nTP: Number of true positives
\nTN: Number of true negatives
\nFP: Number of false positives
\nFN: Number of false negatives
\"This metric is particularly useful when the two classes are imbalanced – that is, one class appears much more than the other.\"
\nhttps://www.statology.org/matthews-correlation-coefficient-python/
\nsklearn.metrics.matthews_corrcoef(y_true, y_pred, *, sample_weight=None)[source]
\nKAGGLE COMPETITIONS:
\nVSB Power Line Fault Detection
\nhttps://www.kaggle.com/competitions/vsb-power-line-fault-detection/overview/evaluation
Bosch Production Line Performance
\nhttps://www.kaggle.com/competitions/bosch-production-line-performance/overview/evaluation
KAGGLE CODES
\nOptimizing probabilities for best MCC By CPMP
\nhttps://www.kaggle.com/code/cpmpml/optimizing-probabilities-for-best-mcc/notebook
R-implementation of MCC optimization By Vopani
\nhttps://www.kaggle.com/code/rohanrao/r-implementation-of-mcc-optimization
5-fold LSTM with threshold tuning [0.618 LB] By Khoi Nguyen
\nhttps://www.kaggle.com/code/suicaokhoailang/5-fold-lstm-with-threshold-tuning-0-618-lb
5-fold LSTM Attention (fully commented) [0.694] By Bruno Aquino
\nhttps://www.kaggle.com/code/braquino/5-fold-lstm-attention-fully-commented-0-694/notebook
VSB Competition : Attention BiLSTM with features By Tarun Paparaju
\nhttps://www.kaggle.com/code/tarunpaparaju/vsb-competition-attention-bilstm-with-features
KAGGLE TOPICS
\nOptimising probabilities -> binary prediction (script) By Anokas
\nhttps://www.kaggle.com/competitions/bosch-production-line-performance/discussion/22917
Matthews Correlation Coefficient topic by delayedkarma
\nhttps://www.kaggle.com/competitions/vsb-power-line-fault-detection/discussion/75457
https://lettier.github.io/posts/2016-08-05-matthews-correlation-coefficient.html
\nhttp://standardwisdom.com/softwarejournal/2010/06/matthews-correlation-coefficient/
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
\nCitation: Chicco, D., Jurman, G. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 21, 6 (2020). https://doi.org/10.1186/s12864-019-6413-7
\n\"To evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet. Accuracy and F1 score computed on confusion matrices have been (and still are) among the most popular adopted metrics in binary classification tasks. However, these statistical measures can dangerously show overoptimistic inflated results, especially on imbalanced datasets.\"
\nACCURACY, MCC, F1 SCORE.
\n\"An effective solution overcoming the class imbalance issue comes from the Matthews correlation coefficient (MCC), a special case of the ϕ phi coefficient. \"
\n\"Originally developed by Matthews in 1975 for comparison of chemical structures , MCC was re-proposed by Baldi and colleagues in 2000 as a standard performance metric for machine learning with a natural extension to the multiclass case.\"
\n\"The criterion of MCC is intuitive and straightforward: to get a high quality score, the classifier has to make correct predictions both on the majority of the negative cases, and on the majority of the positive cases, independently of their ratios in the overall dataset. F1 and accuracy, instead, generate reliable results only when applied to balanced datasets, and produce misleading results when applied to imbalanced cases. For these reasons, the authors suggested all the researchers working with confusion matrices to evaluate their binary classification predictions through the MCC, instead of using F1 score or accuracy.\"
\nhttps://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-019-6413-7
\n\"In statistics, the phi coefficient (or mean square contingency coefficient and denoted by φ or rφ) is a measure of association for two binary variables. In machine learning, it is known as the Matthews correlation coefficient (MCC) and used as a measure of the quality of binary (two-class) classifications, introduced by biochemist Brian W. Matthews in 1975.\"
\nhttps://en.wikipedia.org/wiki/Phi_coefficient
\n\"Sometimes in data science and machine learning we encounter problems of imbalanced classes. These are problems when one class might have more instances than another. This makes accuracy a bad metric. One metric that helps with this problem is Matthew’s Correlation Coefficient (MCC). \"
\nUSING the MCC in PYTHON and R
\n\"Using the MCC in python is very easy. You can just use Scikit Learn’s metrics API. The MCC can be executed through the function matthews_corrcoef. In R, you can use the function mcc from the mltools package.\"
\nhttps://thedatascientist.com/metrics-matthews-correlation-coefficient/
\nMCC = (TPTN – FPFN) / √(TP+FP)(TP+FN)(TN+FP)(TN+FN)
\nTP: Number of true positives
\nTN: Number of true negatives
\nFP: Number of false positives
\nFN: Number of false negatives
\"This metric is particularly useful when the two classes are imbalanced – that is, one class appears much more than the other.\"
\nhttps://www.statology.org/matthews-correlation-coefficient-python/
\nsklearn.metrics.matthews_corrcoef(y_true, y_pred, *, sample_weight=None)[source]
\nKAGGLE COMPETITIONS:
\nVSB Power Line Fault Detection
\nhttps://www.kaggle.com/competitions/vsb-power-line-fault-detection/overview/evaluation
Bosch Production Line Performance
\nhttps://www.kaggle.com/competitions/bosch-production-line-performance/overview/evaluation
KAGGLE CODES
\nOptimizing probabilities for best MCC By CPMP
\nhttps://www.kaggle.com/code/cpmpml/optimizing-probabilities-for-best-mcc/notebook
R-implementation of MCC optimization By Vopani
\nhttps://www.kaggle.com/code/rohanrao/r-implementation-of-mcc-optimization
5-fold LSTM with threshold tuning [0.618 LB] By Khoi Nguyen
\nhttps://www.kaggle.com/code/suicaokhoailang/5-fold-lstm-with-threshold-tuning-0-618-lb
5-fold LSTM Attention (fully commented) [0.694] By Bruno Aquino
\nhttps://www.kaggle.com/code/braquino/5-fold-lstm-attention-fully-commented-0-694/notebook
VSB Competition : Attention BiLSTM with features By Tarun Paparaju
\nhttps://www.kaggle.com/code/tarunpaparaju/vsb-competition-attention-bilstm-with-features
KAGGLE TOPICS
\nOptimising probabilities -> binary prediction (script) By Anokas
\nhttps://www.kaggle.com/competitions/bosch-production-line-performance/discussion/22917
Matthews Correlation Coefficient topic by delayedkarma
\nhttps://www.kaggle.com/competitions/vsb-power-line-fault-detection/discussion/75457
https://lettier.github.io/posts/2016-08-05-matthews-correlation-coefficient.html
\nhttp://standardwisdom.com/softwarejournal/2010/06/matthews-correlation-coefficient/
This is superb and informative. Thanks.
", "rawMarkdown": "This is superb and informative. Thanks.", "votes": null }, { "id": "2060354", "postDate": "12/09/2022 19:22:17", "content": "Thank you R Nasirudeen for your support and comment.
", "rawMarkdown": "Thank you R Nasirudeen for your support and comment.", "votes": null }, { "id": "2105937", "postDate": "01/18/2023 20:45:54", "content": "So informative, Thanks
", "rawMarkdown": "So informative, Thanks", "votes": null }, { "id": "2119848", "postDate": "01/29/2023 06:39:44", "content": "Thank you for the information.
", "rawMarkdown": "Thank you for the information.", "votes": null } ], "comments": [ { "id": 2060312, "author_name": "nasere", "author_url": "", "post_date": "12/09/2022 18:12:17", "content": "This is superb and informative. Thanks.
", "votes": null, "replies": [ { "id": 2060354, "author_name": "mpwolke", "author_url": "", "post_date": "12/09/2022 19:22:17", "content": "Thank you R Nasirudeen for your support and comment.
", "votes": null, "replies": [] } ] }, { "id": 2105937, "author_name": "codingo", "author_url": "", "post_date": "01/18/2023 20:45:54", "content": "So informative, Thanks
", "votes": null, "replies": [] }, { "id": 2119848, "author_name": "dariussingh", "author_url": "", "post_date": "01/29/2023 06:39:44", "content": "Thank you for the information.
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "2056251": "#The advantages of the Matthews correlation coefficient \n\nThe advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation\n\nCitation: Chicco, D., Jurman, G. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 21, 6 (2020). https://doi.org/10.1186/s12864-019-6413-7\n\n\"To evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet. Accuracy and F1 score computed on confusion matrices have been (and still are) among the most popular adopted metrics in binary classification tasks. However, these statistical measures can dangerously show overoptimistic inflated results, especially on imbalanced datasets.\"\n\nACCURACY, MCC, F1 SCORE. \n\n\"An effective solution overcoming the class imbalance issue comes from the Matthews correlation coefficient (MCC), a special case of the ϕ phi coefficient. \"\n\n\"Originally developed by Matthews in 1975 for comparison of chemical structures , MCC was re-proposed by Baldi and colleagues in 2000 as a standard performance metric for machine learning with a natural extension to the multiclass case.\"\n\n\"The criterion of MCC is intuitive and straightforward: to get a high quality score, the classifier has to make correct predictions both on the majority of the negative cases, and on the majority of the positive cases, independently of their ratios in the overall dataset. F1 and accuracy, instead, generate reliable results only when applied to balanced datasets, and produce misleading results when applied to imbalanced cases. For these reasons, the authors suggested all the researchers working with confusion matrices to evaluate their binary classification predictions through the MCC, instead of using F1 score or accuracy.\"\n\nhttps://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-019-6413-7\n\n#The phi coefficient, in Machine Learning is known as the Matthews correlation coefficient (MCC)\n\n\"In statistics, the phi coefficient (or mean square contingency coefficient and denoted by φ or rφ) is a measure of association for two binary variables. In machine learning, it is known as the Matthews correlation coefficient (MCC) and used as a measure of the quality of binary (two-class) classifications, introduced by biochemist Brian W. Matthews in 1975.\"\n\nhttps://en.wikipedia.org/wiki/Phi_coefficient\n\n#Matthew’s correlation coefficient: A metric for imbalanced class problems\n\n\"Sometimes in data science and machine learning we encounter problems of imbalanced classes. These are problems when one class might have more instances than another. This makes accuracy a bad metric. One metric that helps with this problem is Matthew’s Correlation Coefficient (MCC). \"\n\nUSING the MCC in PYTHON and R\n\n\"Using the MCC in python is very easy. You can just use Scikit Learn’s metrics API. The MCC can be executed through the function matthews_corrcoef. In R, you can use the function mcc from the mltools package.\"\n\nhttps://thedatascientist.com/metrics-matthews-correlation-coefficient/\n\n#How to Calculate Matthews Correlation Coefficient in Python\n\nMCC = (TP*TN – FP*FN) / √(TP+FP)(TP+FN)(TN+FP)(TN+FN)\n\nTP: Number of true positives\nTN: Number of true negatives\nFP: Number of false positives\nFN: Number of false negatives\n\n\"This metric is particularly useful when the two classes are imbalanced – that is, one class appears much more than the other.\"\n\nhttps://www.statology.org/matthews-correlation-coefficient-python/\n\n#Sklearn\n\nsklearn.metrics.matthews_corrcoef(y_true, y_pred, *, sample_weight=None)[source]\n\n#Matthews Correlation on Kaggle\n\nKAGGLE COMPETITIONS:\n\nVSB Power Line Fault Detection\nhttps://www.kaggle.com/competitions/vsb-power-line-fault-detection/overview/evaluation\n\nBosch Production Line Performance\nhttps://www.kaggle.com/competitions/bosch-production-line-performance/overview/evaluation\n\nKAGGLE CODES\n\nOptimizing probabilities for best MCC By CPMP\nhttps://www.kaggle.com/code/cpmpml/optimizing-probabilities-for-best-mcc/notebook\n\nR-implementation of MCC optimization By Vopani\nhttps://www.kaggle.com/code/rohanrao/r-implementation-of-mcc-optimization\n\n5-fold LSTM with threshold tuning [0.618 LB] By Khoi Nguyen\nhttps://www.kaggle.com/code/suicaokhoailang/5-fold-lstm-with-threshold-tuning-0-618-lb\n\n5-fold LSTM Attention (fully commented) [0.694] By Bruno Aquino\nhttps://www.kaggle.com/code/braquino/5-fold-lstm-attention-fully-commented-0-694/notebook\n\nVSB Competition : Attention BiLSTM with features By Tarun Paparaju\nhttps://www.kaggle.com/code/tarunpaparaju/vsb-competition-attention-bilstm-with-features\n\nKAGGLE TOPICS\n\nOptimising probabilities -> binary prediction (script) By Anokas\nhttps://www.kaggle.com/competitions/bosch-production-line-performance/discussion/22917\n\nMatthews Correlation Coefficient topic by delayedkarma\nhttps://www.kaggle.com/competitions/vsb-power-line-fault-detection/discussion/75457\n\nhttps://lettier.github.io/posts/2016-08-05-matthews-correlation-coefficient.html\nhttp://standardwisdom.com/softwarejournal/2010/06/matthews-correlation-coefficient/\n\n#I hope it can help you. Good luck with your NFL Player contact Detection to prevent injuries.", "2060312": "This is superb and informative. Thanks.", "2060354": "Thank you R Nasirudeen for your support and comment.", "2105937": "So informative, Thanks", "2119848": "Thank you for the information." }, "source": "meta" }