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Logistic Regression with polynominal attributes
Hi,
Novice here. My member type is a university professor but not in the data science field.
I am trying to run logistic regression using the data downloaded from here: https://www.kaggle.com/fayomi/advertising. As stated in the data description of the site, the goal is to predict people who are more likely to click the ad. I split the data into training and scoring data.
I want to get some help in how to handle the polynominal attributes in the data set. For example, there are so many Ad Topic Line and City values. When I ran the model, it ran but the results did not look right.
Also, the confidence percentage for ad click (yes, no) was either one or zero for all rows. I feel something might be wrong here.
Thanks for help...
Novice here. My member type is a university professor but not in the data science field.
I am trying to run logistic regression using the data downloaded from here: https://www.kaggle.com/fayomi/advertising. As stated in the data description of the site, the goal is to predict people who are more likely to click the ad. I split the data into training and scoring data.
I want to get some help in how to handle the polynominal attributes in the data set. For example, there are so many Ad Topic Line and City values. When I ran the model, it ran but the results did not look right.
Also, the confidence percentage for ad click (yes, no) was either one or zero for all rows. I feel something might be wrong here.
Thanks for help...
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Best Answer
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sgenzer Administrator, Moderator, Employee-RapidMiner, RapidMiner Certified Analyst, Community Manager, Member, University Professor, PM Moderator Posts: 2,959 Community Managerhi @jykim so that was fun. Here's a quick solution.
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<parameter key="max_runtime_seconds" value="0"/> </operator> <connect from_port="training set" to_op="Logistic Regression" to_port="training set"/> <connect from_op="Logistic Regression" from_port="model" to_port="model"/> <portSpacing port="source_training set" spacing="0"/> <portSpacing port="sink_model" spacing="0"/> <portSpacing port="sink_through 1" spacing="0"/> </process> <process expanded="true"> <operator activated="true" class="apply_model" compatibility="9.6.000" expanded="true" height="82" name="Apply Model" width="90" x="45" y="34"> <list key="application_parameters"/> <parameter key="create_view" value="false"/> </operator> <operator activated="true" class="performance_binominal_classification" compatibility="9.6.000" expanded="true" height="82" name="Performance" width="90" x="179" y="34"> <parameter key="manually_set_positive_class" value="false"/> <parameter key="main_criterion" value="first"/> <parameter key="accuracy" value="true"/> <parameter key="classification_error" value="false"/> <parameter key="kappa" value="false"/> <parameter key="AUC (optimistic)" value="false"/> <parameter key="AUC" value="false"/> <parameter key="AUC (pessimistic)" value="false"/> <parameter key="precision" value="false"/> <parameter key="recall" value="false"/> <parameter key="lift" value="false"/> <parameter key="fallout" value="false"/> <parameter key="f_measure" value="false"/> <parameter key="false_positive" value="false"/> <parameter key="false_negative" value="false"/> <parameter key="true_positive" value="false"/> <parameter key="true_negative" value="false"/> <parameter key="sensitivity" value="false"/> <parameter key="specificity" value="false"/> <parameter key="youden" value="false"/> <parameter key="positive_predictive_value" value="false"/> <parameter key="negative_predictive_value" value="false"/> <parameter key="psep" value="false"/> <parameter key="skip_undefined_labels" value="true"/> <parameter key="use_example_weights" value="true"/> </operator> <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="performance 1"/> <portSpacing port="source_model" spacing="0"/> <portSpacing port="source_test set" spacing="0"/> <portSpacing port="source_through 1" spacing="0"/> <portSpacing port="sink_test set results" spacing="0"/> <portSpacing port="sink_performance 1" spacing="0"/> <portSpacing port="sink_performance 2" spacing="0"/> </process> </operator> <connect from_op="Retrieve advertising" from_port="output" to_op="Subprocess" to_port="in 1"/> <connect from_op="Subprocess" from_port="out 1" to_op="Cross Validation" to_port="example set"/> <connect from_op="Cross Validation" from_port="model" to_port="result 1"/> <connect from_op="Cross Validation" 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Scott1
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Scott