DecisionTreeClassifier
The SPAM E-mail Classifier, powered by the DecisionTreeClassifier algorithm, is designed to discern between spam and non-spam emails.
- Hopkins,Mark, Reeber,Erik, Forman,George, and Suermondt,Jaap.(1999). Spambase. UCI Machine Learning Repository.https://doi.org/10.24432/C53G6X.
The SPAM E-mail Classifier, powered by theDecisionTreeClassifier algorithm, is designed to discern between spam and non-spam emails. The concept of "spam" in this context encompasses a wide range of content, including advertisements for products/websites,get-rich-quick schemes, chain letters, and explicit material.
To try this model, select Model 2 in the Privasea client andcopy your feature vector as the X vector. The client will then encrypt this vector on your local machine using your locally stored client key, and the encrypted data will be transmitted to the Privanetix nodes. Subsequent inference operations will take place in the encrypted domain on the Privanetix nodes, and the encrypted result will be sent back to your client. Upon arrival, the result will be decrypted using your locally stored client key. The decrypted result will yield a two-dimensional vector representing the likelihood of the associated tags. For instance, a result like "011,0.89" signifies a classification as true. You can compare it with the provided output in the tables
Features vector：[ '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_%3B', 'char_freq_%28', 'char_freq_%5B', 'char_freq_%21', 'char_freq_%24', 'char_freq_%23', 'capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
Output：[ 0, false 1, true]