I need help for improve my Confusion Matrix
luistops
New Altair Community Member
Hi im new in Rapidminer, and i start a project to clasificate news in 2 types, 1= gender violence and 2= generic news.
This is my transforms etc that i used for :
https://gyazo.com/aae6e06bc6f1f7623881876b6ed06d23
This is my procesing text transformations:
https://gyazo.com/ed3d19158038d8c52f02db98ae13ba67
My cross validation:
https://gyazo.com/af702c35350dcd735ae61bf2f2322586
My result:
https://gyazo.com/a7752521e886c68be0490d3150b78635
Myquestion is how can i improve my result, and also for example if i choose a animal violence news, rapidminer fail and select a animal violence news as gender violence, iit is possible to create a rule so that in case it finds for example the word Animal, Dog, etc classify it as generic news?
Thanks for help, sorry for my english
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0
Answers
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<parameter key="Violencia" value="C:\Noticias de Genero"/>
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<parameter key="divergence" value="GeneralizedIDivergence"/>
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<description align="center" color="yellow" colored="false" height="105" resized="false" width="180" x="11" y="419">Diccionario de sinonimos<br/>Si aparece la palabra perro no es de violencia de genero=violencia animal<br/></description>
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<parameter key="AUC" value="true"/>
<parameter key="AUC (pessimistic)" value="true"/>
<parameter key="precision" value="true"/>
<parameter key="recall" value="true"/>
<parameter key="lift" value="true"/>
<parameter key="fallout" value="true"/>
<parameter key="f_measure" value="true"/>
<parameter key="false_positive" value="true"/>
<parameter key="false_negative" value="true"/>
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<parameter key="true_negative" value="true"/>
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<parameter key="specificity" value="true"/>
<parameter key="youden" value="true"/>
<parameter key="positive_predictive_value" value="true"/>
<parameter key="negative_predictive_value" value="true"/>
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</process>0 -
hello @luistops - ok thanks for that. So for starters to improve your model I would encourage you to optimize your parameters (see https://www.youtube.com/watch?v=nXjjAA2mDMY). As for reducing/combining classes, I'd need to see your dataset to understand better what you're trying to do.
Scott
0 -
Ok thank you so much for help, i include my datashet. my proyect just try to diference and clasificate news in 2 categories:
1. No gender violence
2. Gender violence
0 -
hello @luistops - ok I think I get your idea. I did not have your Spanish stopword dictionary so I just greyed that out. As for animal, dog, etc.. I put some replace token lines in your Process Documents operator that perhaps (?) is what you're looking for. Note that the "test set" I created is rather small so don't get too excited by your 100% accuracy result.
<?xml version="1.0" encoding="UTF-8"?><process version="7.6.001">
<context>
<input/>
<output/>
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<operator activated="true" class="process" compatibility="7.6.001" expanded="true" name="Process">
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<operator activated="true" class="text:process_document_from_file" compatibility="7.5.000" expanded="true" height="82" name="Process Documents from Files" width="90" x="112" y="34">
<list key="text_directories">
<parameter key="Genericas" value="/Users/GenzerConsulting/OneDrive - RapidMiner/OneDrive Repository/random community stuff/newsfromhtml1/No gender violence news"/>
<parameter key="Violencia" value="/Users/GenzerConsulting/OneDrive - RapidMiner/OneDrive Repository/random community stuff/newsfromhtml1/Gender violence news"/>
</list>
<parameter key="file_pattern" value="*txt"/>
<parameter key="encoding" value="UTF-8"/>
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<operator activated="true" class="text:replace_tokens" compatibility="7.5.000" expanded="true" height="68" name="Replace Tokens" width="90" x="45" y="34">
<list key="replace_dictionary">
<parameter key="titleHTML;desHTML;fechaHTML" value=" "/>
<parameter key="TituloLimpio;Descripcion;FechaF" value=" "/>
<parameter key="animal.*violence" value="generic news"/>
<parameter key="dog.*violence" value="generic news"/>
</list>
</operator>
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<parameter key="file" value="D:\stopWordsSpanish.txt"/>
<parameter key="encoding" value="UTF-8"/>
</operator>
<operator activated="true" class="text:stem_snowball" compatibility="7.5.000" expanded="true" height="68" name="Stem (Snowball)" width="90" x="447" y="34">
<parameter key="language" value="Spanish"/>
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<portSpacing port="sink_model" spacing="0"/>
<portSpacing port="sink_through 1" spacing="0"/>
<description align="center" color="yellow" colored="false" height="105" resized="false" width="180" x="11" y="419">Diccionario de sinonimos<br/>Si aparece la palabra perro no es de violencia de genero=violencia animal<br/></description>
<description align="center" color="yellow" colored="false" height="105" resized="false" width="180" x="137" y="403">Type your comment</description>
</process>
<process expanded="true">
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<operator activated="true" class="performance_binominal_classification" compatibility="7.6.001" expanded="true" height="82" name="Performance (train)" width="90" x="179" y="34">
<parameter key="main_criterion" value="accuracy"/>
<parameter key="classification_error" value="true"/>
<parameter key="kappa" value="true"/>
<parameter key="AUC (optimistic)" value="true"/>
<parameter key="AUC" value="true"/>
<parameter key="AUC (pessimistic)" value="true"/>
<parameter key="precision" value="true"/>
<parameter key="recall" value="true"/>
<parameter key="lift" value="true"/>
<parameter key="fallout" value="true"/>
<parameter key="f_measure" value="true"/>
<parameter key="false_positive" value="true"/>
<parameter key="false_negative" value="true"/>
<parameter key="true_positive" value="true"/>
<parameter key="true_negative" value="true"/>
<parameter key="sensitivity" value="true"/>
<parameter key="specificity" value="true"/>
<parameter key="youden" value="true"/>
<parameter key="positive_predictive_value" value="true"/>
<parameter key="negative_predictive_value" value="true"/>
<parameter key="psep" value="true"/>
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<connect from_port="model" to_op="Apply Model" to_port="model"/>
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<list key="class_weights"/>
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<connect from_op="Process Documents from Files" from_port="example set" to_op="Split Data" to_port="example set"/>
<connect from_op="Split Data" from_port="partition 1" to_op="Optimize Parameters (Grid)" to_port="input 1"/>
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<connect from_op="Optimize Parameters (Grid)" from_port="performance" to_port="result 2"/>
<connect from_op="Optimize Parameters (Grid)" from_port="parameter" to_port="result 3"/>
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<connect from_op="Optimize Parameters (Grid)" from_port="result 2" to_port="result 1"/>
<connect from_op="Apply Model (2)" from_port="labelled data" to_op="Performance (test)" to_port="labelled data"/>
<connect from_op="Performance (test)" from_port="performance" to_port="result 4"/>
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</operator>
</process>Scott
0 -
Just looking through your initial confusion matrix you had some decent results off the bat. What k value were you using?
In conjuction with what @sgenzer said, optimization is a must step. I would also look at using the Deep Learning algo as well as a LinearSVM too.
1 -
Thank you for the help i used k=2, now i have one doubt i have 2 result, but i dont understand where its come from, i have this result https://gyazo.com/00d0fc3aa5ff57cc7eb20b8d6bc45bca thats its great.
And i have this one https://gyazo.com/79891aae0d4958d6d9b790d7bc934557
Whats is the diferent of this result because i think they use the same algorithm to calculate the result
Again thanks
0 -
So there's another tab in the Results view that you need to look at: ParameterSet (Optimize Parameters (Grid)). This will tell you the value of k (assuming you used my process) that was optimal.
Scott
0
Altair Employee