I have built a process like this, with little knowledge about Naive Bayes (Kernel)'s setup.
<process version="5.1.006">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="5.1.006" expanded="true" name="Process">
<process expanded="true" height="476" width="815">
<operator activated="true" class="text:process_document_from_file" compatibility="5.1.001" expanded="true" height="76" name="Process Documents from Files" width="90" x="45" y="30">
<list key="text_directories">
<parameter key="positive" value="C:\Documents and Settings\TU001YU\Desktop\positive"/>
<parameter key="negative" value="C:\Documents and Settings\TU001YU\Desktop\negative"/>
</list>
<process expanded="true" height="524" width="806">
<operator activated="true" class="text:transform_cases" compatibility="5.1.001" expanded="true" height="60" name="Transform Cases" width="90" x="179" y="30"/>
<operator activated="true" class="text:tokenize" compatibility="5.1.001" expanded="true" height="60" name="Tokenize" width="90" x="447" y="30"/>
<connect from_port="document" to_op="Transform Cases" to_port="document"/>
<connect from_op="Transform Cases" from_port="document" to_op="Tokenize" to_port="document"/>
<connect from_op="Tokenize" from_port="document" to_port="document 1"/>
<portSpacing port="source_document" spacing="0"/>
<portSpacing port="sink_document 1" spacing="0"/>
<portSpacing port="sink_document 2" spacing="0"/>
</process>
</operator>
<operator activated="true" class="naive_bayes_kernel" compatibility="5.1.006" expanded="true" height="76" name="Naive Bayes (Kernel)" width="90" x="447" y="30"/>
<operator activated="true" class="read_excel" compatibility="5.1.006" expanded="true" height="60" name="Read Excel" width="90" x="45" y="300">
<parameter key="excel_file" value="C:\Documents and Settings\TU001YU\Desktop\BBCNewsFeeds.xls"/>
<parameter key="imported_cell_range" value="A1"/>
<list key="annotations"/>
<list key="data_set_meta_data_information"/>
</operator>
<operator activated="true" class="nominal_to_text" compatibility="5.1.006" expanded="true" height="76" name="Nominal to Text" width="90" x="179" y="300"/>
<operator activated="true" class="text:data_to_documents" compatibility="5.1.001" expanded="true" height="60" name="Data to Documents" width="90" x="313" y="300">
<list key="specify_weights"/>
</operator>
<operator activated="true" class="text:process_documents" compatibility="5.1.001" expanded="true" height="94" name="Process Documents" width="90" x="447" y="165">
<process expanded="true" height="524" width="806">
<operator activated="true" class="text:transform_cases" compatibility="5.1.001" expanded="true" height="60" name="Transform Cases (2)" width="90" x="112" y="30"/>
<operator activated="true" class="text:tokenize" compatibility="5.1.001" expanded="true" height="60" name="Tokenize (2)" width="90" x="246" y="30"/>
<operator activated="true" class="text:stem_porter" compatibility="5.1.001" expanded="true" height="60" name="Stem (Porter)" width="90" x="380" y="30"/>
<operator activated="true" class="text:filter_stopwords_english" compatibility="5.1.001" expanded="true" height="60" name="Filter Stopwords (2)" width="90" x="514" y="30"/>
<operator activated="true" class="text:filter_by_length" compatibility="5.1.001" expanded="true" height="60" name="Filter Tokens (by Length)" width="90" x="648" y="30">
<parameter key="min_chars" value="2"/>
<parameter key="max_chars" value="99"/>
</operator>
<connect from_port="document" to_op="Transform Cases (2)" to_port="document"/>
<connect from_op="Transform Cases (2)" from_port="document" to_op="Tokenize (2)" to_port="document"/>
<connect from_op="Tokenize (2)" from_port="document" to_op="Stem (Porter)" to_port="document"/>
<connect from_op="Stem (Porter)" from_port="document" to_op="Filter Stopwords (2)" to_port="document"/>
<connect from_op="Filter Stopwords (2)" from_port="document" to_op="Filter Tokens (by Length)" to_port="document"/>
<connect from_op="Filter Tokens (by Length)" from_port="document" to_port="document 1"/>
<portSpacing port="source_document" spacing="0"/>
<portSpacing port="sink_document 1" spacing="0"/>
<portSpacing port="sink_document 2" spacing="0"/>
</process>
</operator>
<operator activated="true" class="apply_model" compatibility="5.1.006" expanded="true" height="76" name="Apply Model" width="90" x="648" y="30">
<list key="application_parameters"/>
</operator>
<connect from_op="Process Documents from Files" from_port="example set" to_op="Naive Bayes (Kernel)" to_port="training set"/>
<connect from_op="Process Documents from Files" from_port="word list" to_op="Process Documents" to_port="word list"/>
<connect from_op="Naive Bayes (Kernel)" from_port="model" to_op="Apply Model" to_port="model"/>
<connect from_op="Read Excel" from_port="output" to_op="Nominal to Text" to_port="example set input"/>
<connect from_op="Nominal to Text" from_port="example set output" to_op="Data to Documents" to_port="example set"/>
<connect from_op="Data to Documents" from_port="documents" to_op="Process Documents" to_port="documents 1"/>
<connect from_op="Process Documents" from_port="example set" to_op="Apply Model" to_port="unlabelled data"/>
<connect from_op="Apply Model" from_port="labelled data" to_port="result 1"/>
<connect from_op="Apply Model" from_port="model" to_port="result 2"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
<portSpacing port="sink_result 2" spacing="0"/>
<portSpacing port="sink_result 3" spacing="0"/>
</process>
</operator>
</process>
I would like some help on how I can improve upon this current process, as well as how exactly does Naive Bayes Kernel calculate its Confidence?
I inserted two txt files each containing a list of words. Then I process an excel file and try to classify each row. I took a look at wikipedia for how Naive Bayes works already, but I am not sure how the Kernel one works.
Could anyone provide me a formula of how RM calculates this?
For example, if a row in an excel file contains 5 words that match with Postive and 4 words that match with Negative, and 11 words that do not much either of those, how would Naive Bayes Kernel calculate this and classify this?
Thank you very much, I am a CS student doing internship, so not quite know the maths side.
