How to calculate Accuracy, Precision, Recall and F1 score based on predict_proba matrix?
$begingroup$
I found this link that defines Accuracy, Precision, Recall and F1 score as:
Accuracy: the percentage of texts that were predicted with the correct tag.
Precision: the percentage of examples the classifier got right out of the total number of examples that it predicted for a given tag.
Recall: the percentage of examples the classifier predicted for a given tag out of the total number of examples it should have predicted for that given tag.
F1 Score: the harmonic mean of precision and recall.
Following this question of mine, my MultinomialNB classifier calculated the predict_proba matrix for the test set as follows:
0.192995 0.0996929 0.173688 0.136715 0.126616 0.133012 0.137282
0.174185 0.109345 0.169467 0.144389 0.115021 0.132762 0.154831
0.14172 0.190075 0.125429 0.155343 0.122939 0.149733 0.114763
0.130958 0.2304 0.108793 0.174371 0.115698 0.122529 0.117251
0.139486 0.0938475 0.236573 0.133689 0.118372 0.165151 0.112881
0.135901 0.0845106 0.262501 0.127767 0.119785 0.166609 0.102926
0.136622 0.13782 0.119651 0.320522 0.0854596 0.0996346 0.100292
0.139607 0.181654 0.112189 0.259983 0.0920986 0.106649 0.107819
0.151441 0.0929748 0.155358 0.130407 0.208591 0.151803 0.109425
0.132648 0.122881 0.130545 0.126466 0.196319 0.142594 0.148548
0.135545 0.101456 0.177762 0.118609 0.120773 0.253616 0.0922385
0.132612 0.112645 0.111808 0.102153 0.113548 0.327516 0.0997178
0.111618 0.0859541 0.106807 0.116613 0.085918 0.0873931 0.405696
0.107745 0.0936872 0.0877116 0.122336 0.0902212 0.0909265 0.407373
1. The Answerer of my last question, said that although the predict_proba matrix elements are all less than 0.5, they may be useful in text labeling. But From the above definitions, I concluded that the Accuracy and Precision of the prediction is zero, since all of the predicted values are less than 0.5. Am I correct?
2. I'm not sure about the Recall and F1 score and how to calculate them.
3. How can I interpret the matrix and the model's usefulness?
classification scikit-learn nlp naive-bayes-classifier
asked 17 mins ago
hyTuevhyTuev
535
$endgroup$
add a comment |
$begingroup$
I found this link that defines Accuracy, Precision, Recall and F1 score as:
Accuracy: the percentage of texts that were predicted with the correct tag.
Precision: the percentage of examples the classifier got right out of the total number of examples that it predicted for a given tag.
Recall: the percentage of examples the classifier predicted for a given tag out of the total number of examples it should have predicted for that given tag.
F1 Score: the harmonic mean of precision and recall.
Following this question of mine, my MultinomialNB classifier calculated the predict_proba matrix for the test set as follows:
0.192995 0.0996929 0.173688 0.136715 0.126616 0.133012 0.137282
0.174185 0.109345 0.169467 0.144389 0.115021 0.132762 0.154831
0.14172 0.190075 0.125429 0.155343 0.122939 0.149733 0.114763
0.130958 0.2304 0.108793 0.174371 0.115698 0.122529 0.117251
0.139486 0.0938475 0.236573 0.133689 0.118372 0.165151 0.112881
0.135901 0.0845106 0.262501 0.127767 0.119785 0.166609 0.102926
0.136622 0.13782 0.119651 0.320522 0.0854596 0.0996346 0.100292
0.139607 0.181654 0.112189 0.259983 0.0920986 0.106649 0.107819
0.151441 0.0929748 0.155358 0.130407 0.208591 0.151803 0.109425
0.132648 0.122881 0.130545 0.126466 0.196319 0.142594 0.148548
0.135545 0.101456 0.177762 0.118609 0.120773 0.253616 0.0922385
0.132612 0.112645 0.111808 0.102153 0.113548 0.327516 0.0997178
0.111618 0.0859541 0.106807 0.116613 0.085918 0.0873931 0.405696
0.107745 0.0936872 0.0877116 0.122336 0.0902212 0.0909265 0.407373
1. The Answerer of my last question, said that although the predict_proba matrix elements are all less than 0.5, they may be useful in text labeling. But From the above definitions, I concluded that the Accuracy and Precision of the prediction is zero, since all of the predicted values are less than 0.5. Am I correct?
2. I'm not sure about the Recall and F1 score and how to calculate them.
3. How can I interpret the matrix and the model's usefulness?
classification scikit-learn nlp naive-bayes-classifier
asked 17 mins ago
hyTuevhyTuev
535
$endgroup$
add a comment |
$begingroup$
I found this link that defines Accuracy, Precision, Recall and F1 score as:
Accuracy: the percentage of texts that were predicted with the correct tag.
Precision: the percentage of examples the classifier got right out of the total number of examples that it predicted for a given tag.
Recall: the percentage of examples the classifier predicted for a given tag out of the total number of examples it should have predicted for that given tag.
F1 Score: the harmonic mean of precision and recall.
Following this question of mine, my MultinomialNB classifier calculated the predict_proba matrix for the test set as follows:
0.192995 0.0996929 0.173688 0.136715 0.126616 0.133012 0.137282
0.174185 0.109345 0.169467 0.144389 0.115021 0.132762 0.154831
0.14172 0.190075 0.125429 0.155343 0.122939 0.149733 0.114763
0.130958 0.2304 0.108793 0.174371 0.115698 0.122529 0.117251
0.139486 0.0938475 0.236573 0.133689 0.118372 0.165151 0.112881
0.135901 0.0845106 0.262501 0.127767 0.119785 0.166609 0.102926
0.136622 0.13782 0.119651 0.320522 0.0854596 0.0996346 0.100292
0.139607 0.181654 0.112189 0.259983 0.0920986 0.106649 0.107819
0.151441 0.0929748 0.155358 0.130407 0.208591 0.151803 0.109425
0.132648 0.122881 0.130545 0.126466 0.196319 0.142594 0.148548
0.135545 0.101456 0.177762 0.118609 0.120773 0.253616 0.0922385
0.132612 0.112645 0.111808 0.102153 0.113548 0.327516 0.0997178
0.111618 0.0859541 0.106807 0.116613 0.085918 0.0873931 0.405696
0.107745 0.0936872 0.0877116 0.122336 0.0902212 0.0909265 0.407373
1. The Answerer of my last question, said that although the predict_proba matrix elements are all less than 0.5, they may be useful in text labeling. But From the above definitions, I concluded that the Accuracy and Precision of the prediction is zero, since all of the predicted values are less than 0.5. Am I correct?
2. I'm not sure about the Recall and F1 score and how to calculate them.
3. How can I interpret the matrix and the model's usefulness?
classification scikit-learn nlp naive-bayes-classifier
asked 17 mins ago
hyTuevhyTuev
535
$endgroup$
I found this link that defines Accuracy, Precision, Recall and F1 score as:
Accuracy: the percentage of texts that were predicted with the correct tag.
Precision: the percentage of examples the classifier got right out of the total number of examples that it predicted for a given tag.
Recall: the percentage of examples the classifier predicted for a given tag out of the total number of examples it should have predicted for that given tag.
F1 Score: the harmonic mean of precision and recall.
Following this question of mine, my MultinomialNB classifier calculated the predict_proba matrix for the test set as follows:
0.192995 0.0996929 0.173688 0.136715 0.126616 0.133012 0.137282
0.174185 0.109345 0.169467 0.144389 0.115021 0.132762 0.154831
0.14172 0.190075 0.125429 0.155343 0.122939 0.149733 0.114763
0.130958 0.2304 0.108793 0.174371 0.115698 0.122529 0.117251
0.139486 0.0938475 0.236573 0.133689 0.118372 0.165151 0.112881
0.135901 0.0845106 0.262501 0.127767 0.119785 0.166609 0.102926
0.136622 0.13782 0.119651 0.320522 0.0854596 0.0996346 0.100292
0.139607 0.181654 0.112189 0.259983 0.0920986 0.106649 0.107819
0.151441 0.0929748 0.155358 0.130407 0.208591 0.151803 0.109425
0.132648 0.122881 0.130545 0.126466 0.196319 0.142594 0.148548
0.135545 0.101456 0.177762 0.118609 0.120773 0.253616 0.0922385
0.132612 0.112645 0.111808 0.102153 0.113548 0.327516 0.0997178
0.111618 0.0859541 0.106807 0.116613 0.085918 0.0873931 0.405696
0.107745 0.0936872 0.0877116 0.122336 0.0902212 0.0909265 0.407373
1. The Answerer of my last question, said that although the predict_proba matrix elements are all less than 0.5, they may be useful in text labeling. But From the above definitions, I concluded that the Accuracy and Precision of the prediction is zero, since all of the predicted values are less than 0.5. Am I correct?
2. I'm not sure about the Recall and F1 score and how to calculate them.
3. How can I interpret the matrix and the model's usefulness?
classification scikit-learn nlp naive-bayes-classifier
classification scikit-learn nlp naive-bayes-classifier
asked 17 mins ago
hyTuevhyTuev
535
asked 17 mins ago
hyTuevhyTuev
535
edited 10 mins ago
hyTuev
asked 17 mins ago
hyTuevhyTuev
535
asked 17 mins ago
hyTuevhyTuev
535
asked 17 mins ago
hyTuevhyTuev
535
535
add a comment |
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