Quick solution, what is the specificity, sensitivity, error rate, F1 score, accuracy, can you explain the confusion matrix last time it was wrong in terms of TN, TP, FN, FP also an explanation for how the algorithm calculated the: Correctly Classified Instances Incorrectly Classified Instances: Kappa statistic: Mean absolute error: Root mean squared error? === Run information ===

Scheme: weka.classifiers.bayes.NaiveBayes Relation: spambase Instances: 4601 Attributes: 58 word_freq_make word_freq_address word_freq_all word_freq_3d word_freq_our word_freq_over word_freq_remove word_freq_internet word_freq_order word_freq_mail word_freq_receive word_freq_will word_freq_people word_freq_report word_freq_addresses word_freq_free word_freq_business word_freq_email word_freq_you word_freq_credit word_freq_your word_freq_font word_freq_000 word_freq_money word_freq_hp word_freq_hpl word_freq_george word_freq_650 word_freq_lab word_freq_labs word_freq_telnet word_freq_857 word_freq_data word_freq_415 word_freq_85 word_freq_technology word_freq_1999 word_freq_parts word_freq_pm word_freq_direct word_freq_cs word_freq_meeting word_freq_original word_freq_project word_freq_re word_freq_edu word_freq_table word_freq_conference char_freq_; char_freq_( char_freq_[ char_freq_! char_freq_$ char_freq_# capital_run_length_average capital_run_length_longest capital_run_length_total class Test mode: split 70.0% train, remainder test

=== Classifier model (full training set) ===

Naive Bayes Classifier

Class Attribute 0 1 (0.61) (0.39) =============================================== word_freq_make mean 0.0734 0.1524 std. dev. 0.2975 0.3111 weight sum 2788 1813 precision 0.0322 0.0322

word_freq_address mean 0.2446 0.165 std. dev. 1.6328 0.3509 weight sum 2788 1813 precision 0.084 0.084

word_freq_all mean 0.2007 0.4039 std. dev. 0.5032 0.4808 weight sum 2788 1813 precision 0.0239 0.0239

word_freq_3d mean 0.0007 0.1636 std. dev. 0.1699 2.2174 weight sum 2788 1813 precision 1.0193 1.0193

word_freq_our mean 0.1809 0.5141 std. dev. 0.6145 0.7072 weight sum 2788 1813 precision 0.0394 0.0394

word_freq_over mean 0.0445 0.1749 std. dev. 0.2232 0.3211 weight sum 2788 1813 precision 0.042 0.042

word_freq_remove mean 0.0094 0.2753 std. dev. 0.111 0.5724 weight sum 2788 1813 precision 0.0423 0.0423

word_freq_internet mean 0.0382 0.2088 std. dev. 0.2464 0.5444 weight sum 2788 1813 precision 0.0657 0.0657

word_freq_order mean 0.0381 0.17 std. dev. 0.1986 0.3552 weight sum 2788 1813 precision 0.0368 0.0368

word_freq_mail mean 0.1674 0.351 std. dev. 0.6433 0.632 weight sum 2788 1813 precision 0.0745 0.0745

word_freq_receive mean 0.0217 0.1182 std. dev. 0.1499 0.2507 weight sum 2788 1813 precision 0.0233 0.0233

word_freq_will mean 0.5364 0.5499 std. dev. 0.9789 0.6409 weight sum 2788 1813 precision 0.0307 0.0307

word_freq_people mean 0.0616 0.1435 std. dev. 0.2589 0.3504 weight sum 2788 1813 precision 0.0354 0.0354

word_freq_credit mean 0.0075 0.2048 std. dev. 0.0976 0.7879 weight sum 2788 1813 precision 0.1237 0.1237

capital_run_length_total mean 161.217 470.7076 std. dev. 355.8033 824.8534 weight sum 2788 1813 precision 17.2549 17.2549

Time taken to build model: 0.02 seconds

=== Evaluation on test split ===

Time taken to test model on test split: 0.02 seconds

=== Summary ===

Correctly Classified Instances 1076 77.971 % Incorrectly Classified Instances 304 22.029 % Kappa statistic 0.5734 Mean absolute error 0.2204 Root mean squared error 0.4677 Relative absolute error 46.1747 % Root relative squared error 95.8285 % Total Number of Instances 1380

=== Detailed Accuracy By Class ===

TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class 0.668 0.046 0.957 0.668 0.787 0.614 0.939 0.944 0 0.954 0.332 0.649 0.954 0.772 0.614 0.942 0.896 1 Weighted Avg. 0.780 0.158 0.837 0.780 0.781 0.614 0.940 0.925

=== Confusion Matrix ===

a b <-- classified as 561 279 | a = 0 25 515 | b = 1