How to view data
Hi,
Another new fan of this great piece of software...
Anyways, I am currently working on a multiclassification problem.
Of which I have been able to get pretty decent perfromance..
So, I have the performance matrix which tells me the predicted class vs the true class and then example set which contains the list of attributes etc.
But I want to see which example is assigned to which class instead of just the overview which the performance matrix gives.
Because in the end, I am more interested on seeing the "live" example result rather than the statistical measures.
Any clue how can I view that
Thanks
Another new fan of this great piece of software...
Anyways, I am currently working on a multiclassification problem.
Of which I have been able to get pretty decent perfromance..
So, I have the performance matrix which tells me the predicted class vs the true class and then example set which contains the list of attributes etc.
But I want to see which example is assigned to which class instead of just the overview which the performance matrix gives.
Because in the end, I am more interested on seeing the "live" example result rather than the statistical measures.
Any clue how can I view that
Thanks
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Hi Andrew,
Thanks for replying. Can you please explain what was that "xml" looking script..
I am mostly working on gui.
I had data saved as csv file
So i imported that csv... did the X-validation..
In X-validation I applied k-nn model.. and thats it.
I never wrote any script..
Thanks for replying. Can you please explain what was that "xml" looking script..
I am mostly working on gui.
I had data saved as csv file
So i imported that csv... did the X-validation..
In X-validation I applied k-nn model.. and thats it.
I never wrote any script..
in rapidminer
look for the process tab (middle, near top)
there should be an xml tab beside that
you can copy and paste xml in there and hit the green checkmark and it will use that as your process
it overwrites whatever is in the current process so best to do it on a new, blank process
look for the process tab (middle, near top)
there should be an xml tab beside that
you can copy and paste xml in there and hit the green checkmark and it will use that as your process
it overwrites whatever is in the current process so best to do it on a new, blank process
Hi Neil,
Thanks!! Also just wanted to convey that I used the info from your blog only to get started (k-NN for document classification).
A quick question.
You usedthe cosine similarity for a measure in k-NN
But I am not able to do so..:(
whenever I try that it says the attributes are not numeric.. Hence i end up using the default measure...
I have like watched that video so many times on hope that maybe i missed something but am not able to spot that out.
I reckon I have to specify when I am importing the data file.
I have data as a csv file??
Thanks
Thanks!! Also just wanted to convey that I used the info from your blog only to get started (k-NN for document classification).
A quick question.
You usedthe cosine similarity for a measure in k-NN
But I am not able to do so..:(
whenever I try that it says the attributes are not numeric.. Hence i end up using the default measure...
I have like watched that video so many times on hope that maybe i missed something but am not able to spot that out.
I reckon I have to specify when I am importing the data file.
I have data as a csv file??
Thanks
In case it helps..
Here is my xml file..
Here is my xml file..
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.1.008">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="5.1.008" expanded="true" name="Process">
<parameter key="parallelize_main_process" value="true"/>
<process expanded="true" height="521" width="815">
<operator activated="true" class="read_csv" compatibility="5.1.008" expanded="true" height="60" name="Read CSV" width="90" x="112" y="75">
<parameter key="csv_file" value="/Users/mohitdeepsingh/Desktop/RMData/data_10k_cleanse.csv"/>
<parameter key="column_separators" value=","/>
<parameter key="first_row_as_names" value="false"/>
<list key="annotations">
<parameter key="0" value="Comment"/>
</list>
<parameter key="encoding" value="MacRoman"/>
<list key="data_set_meta_data_information">
<parameter key="0" value="att1.true.attribute_value.attribute"/>
<parameter key="1" value="att2.true.attribute_value.attribute"/>
<parameter key="2" value="att3.true.attribute_value.attribute"/>
<parameter key="3" value="att4.true.attribute_value.attribute"/>
<parameter key="4" value="att5.true.attribute_value.attribute"/>
<parameter key="5" value="att6.true.attribute_value.attribute"/>
<parameter key="6" value="att7.true.attribute_value.attribute"/>
<parameter key="7" value="att8.true.attribute_value.attribute"/>
<parameter key="8" value="att9.true.attribute_value.attribute"/>
<parameter key="9" value="att10.true.attribute_value.attribute"/>
<parameter key="10" value="att11.true.attribute_value.attribute"/>
<parameter key="11" value="att12.true.attribute_value.attribute"/>
<parameter key="12" value="att13.true.attribute_value.label"/>
</list>
</operator>
<operator activated="true" class="text:process_document_from_data" compatibility="5.1.002" expanded="true" height="76" name="Process Documents from Data" width="90" x="112" y="255">
<list key="specify_weights"/>
<process expanded="true" height="586" width="922">
<operator activated="true" class="web:extract_html_text_content" compatibility="5.1.002" expanded="true" height="60" name="Extract Content" width="90" x="112" y="30"/>
<operator activated="true" class="text:transform_cases" compatibility="5.1.002" expanded="true" height="60" name="Transform Cases" width="90" x="112" y="120"/>
<operator activated="true" class="text:replace_tokens" compatibility="5.1.002" expanded="true" height="60" name="Replace Tokens" width="90" x="112" y="255">
<list key="replace_dictionary"/>
</operator>
<operator activated="true" class="text:tokenize" compatibility="5.1.002" expanded="true" height="60" name="Tokenize" width="90" x="112" y="390"/>
<operator activated="true" class="text:filter_stopwords_english" compatibility="5.1.002" expanded="true" height="60" name="Filter Stopwords (English)" width="90" x="380" y="75"/>
<operator activated="true" class="text:stem_snowball" compatibility="5.1.002" expanded="true" height="60" name="Stem (Snowball)" width="90" x="380" y="210"/>
<operator activated="true" class="text:filter_by_length" compatibility="5.1.002" expanded="true" height="60" name="Filter Tokens (by Length)" width="90" x="380" y="345">
<parameter key="min_chars" value="2"/>
<parameter key="max_chars" value="99"/>
</operator>
<connect from_port="document" to_op="Extract Content" to_port="document"/>
<connect from_op="Extract Content" from_port="document" to_op="Transform Cases" to_port="document"/>
<connect from_op="Transform Cases" from_port="document" to_op="Replace Tokens" to_port="document"/>
<connect from_op="Replace Tokens" from_port="document" to_op="Tokenize" to_port="document"/>
<connect from_op="Tokenize" from_port="document" to_op="Filter Stopwords (English)" to_port="document"/>
<connect from_op="Filter Stopwords (English)" from_port="document" to_op="Stem (Snowball)" to_port="document"/>
<connect from_op="Stem (Snowball)" 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="select_attributes" compatibility="5.1.008" expanded="true" height="76" name="Select Attributes" width="90" x="246" y="435">
<parameter key="attribute_filter_type" value="no_missing_values"/>
</operator>
<operator activated="true" class="x_validation" compatibility="5.1.008" expanded="true" height="130" name="Validation" width="90" x="648" y="165">
<parameter key="number_of_validations" value="100"/>
<parameter key="use_local_random_seed" value="true"/>
<parameter key="parallelize_training" value="true"/>
<parameter key="parallelize_testing" value="true"/>
<process expanded="true" height="586" width="436">
<operator activated="true" class="k_nn" compatibility="5.1.008" expanded="true" height="76" name="k-NN" width="90" x="112" y="30">
<parameter key="k" value="5"/>
<parameter key="weighted_vote" value="true"/>
<parameter key="numerical_measure" value="CosineSimilarity"/>
<parameter key="divergence" value="SquaredEuclideanDistance"/>
</operator>
<connect from_port="training" to_op="k-NN" to_port="training set"/>
<connect from_op="k-NN" from_port="model" to_port="model"/>
<portSpacing port="source_training" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
<portSpacing port="sink_through 1" spacing="0"/>
</process>
<process expanded="true" height="586" width="436">
<operator activated="true" class="apply_model" compatibility="5.1.008" expanded="true" height="76" name="Apply Model" width="90" x="112" y="30">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="performance" compatibility="5.1.008" expanded="true" height="76" name="Performance" width="90" x="87" y="321"/>
<connect from_port="model" to_op="Apply Model" to_port="model"/>
<connect from_port="test set" to_op="Apply Model" to_port="unlabelled data"/>
<connect from_op="Apply Model" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
<connect from_op="Performance" from_port="performance" to_port="averagable 1"/>
<portSpacing port="source_model" spacing="0"/>
<portSpacing port="source_test set" spacing="0"/>
<portSpacing port="source_through 1" spacing="0"/>
<portSpacing port="sink_averagable 1" spacing="0"/>
<portSpacing port="sink_averagable 2" spacing="0"/>
<portSpacing port="sink_averagable 3" spacing="0"/>
</process>
</operator>
<connect from_op="Read CSV" from_port="output" to_op="Process Documents from Data" to_port="example set"/>
<connect from_op="Process Documents from Data" from_port="example set" to_op="Select Attributes" to_port="example set input"/>
<connect from_op="Select Attributes" from_port="example set output" to_op="Validation" to_port="training"/>
<connect from_op="Validation" from_port="model" to_port="result 1"/>
<connect from_op="Validation" from_port="training" to_port="result 2"/>
<connect from_op="Validation" from_port="averagable 1" to_port="result 3"/>
<connect from_op="Validation" from_port="averagable 2" to_port="result 4"/>
<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"/>
<portSpacing port="sink_result 4" spacing="0"/>
<portSpacing port="sink_result 5" spacing="0"/>
</process>
</operator>
</process>
if you use the TF-IDF metric, then your attributes should be numeric, and you can calculate the cosine similarity
otherwise, you could try forcing them to numeric using the nominal to numeric operator
HTH
NM
otherwise, you could try forcing them to numeric using the nominal to numeric operator
HTH
NM
You have to use the apply model operator with the trained model and the unlabelled data you want to predict.
Here's a fake example that shows this. It also shows the performance of this and highlights overfitting. regards
Andrew