"Validate Accuracy with ID3 algorithm"

filanfilan Member Posts: 11 Contributor I
edited June 2019 in Help

Hi all, I am trying to validate a decision tree learning model but the result I got is just 30%, not sure where I did wrong.


I am using the UCI Iris data set and randomly selected 50 instances as my sample.

Then, I discretize the data with the operator "discretize by binning".


Next, dragged in "Apply Model" and "Performance".


Lastly, I randomly pick 10 instance from the original data set as the test data.


Here is my setup.

Screen Shot 2017-03-16 at 1.36.28 AM.png


This is the result i get.

Screen Shot 2017-03-16 at 1.37.56 AM.png


Apparently I get 30% accuracy as the result.

Does it mean my model is poorly design?

If my model is correct, how can I conclude the result? 30% is rather poor right?

Best Answer

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    Thomas_OttThomas_Ott RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,761 Unicorn
    Solution Accepted

    Embed the ID3 algo inside a Cross Validation operator to get an honest evaluation of this process.  You haven't done any splitting of training and testing sets, you just trained the model on the entire dataset and can't realistically get a performance. 


    <?xml version="1.0" encoding="UTF-8"?><process version="7.4.000">
    <operator activated="true" class="process" compatibility="7.4.000" expanded="true" name="Process">
    <process expanded="true">
    <operator activated="true" class="retrieve" compatibility="7.4.000" expanded="true" height="68" name="Retrieve Iris Data" width="90" x="45" y="34">
    <parameter key="repository_entry" value="//Local Repository/Work/Iris Data"/>
    <operator activated="true" class="discretize_by_bins" compatibility="7.4.000" expanded="true" height="103" name="Discretize" width="90" x="179" y="34">
    <parameter key="number_of_bins" value="3"/>
    <operator activated="true" class="concurrency:cross_validation" compatibility="7.4.000" expanded="true" height="145" name="Validation" width="90" x="380" y="34">
    <parameter key="sampling_type" value="stratified sampling"/>
    <process expanded="true">
    <operator activated="true" class="id3" compatibility="7.4.000" expanded="true" height="82" name="ID3" width="90" x="179" y="34"/>
    <connect from_port="training set" to_op="ID3" to_port="training set"/>
    <connect from_op="ID3" 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"/>
    <description align="left" color="green" colored="true" height="80" resized="true" width="248" x="37" y="137">In the training phase, a model is built on the current training data set. (90 % of data by default, 10 times)</description>
    <process expanded="true">
    <operator activated="true" class="apply_model" compatibility="7.4.000" expanded="true" height="82" name="Apply Model (2)" width="90" x="45" y="34">
    <list key="application_parameters"/>
    <operator activated="true" class="performance" compatibility="7.4.000" expanded="true" height="82" name="Performance (2)" width="90" x="179" y="34"/>
    <connect from_port="model" to_op="Apply Model (2)" to_port="model"/>
    <connect from_port="test set" to_op="Apply Model (2)" to_port="unlabelled data"/>
    <connect from_op="Apply Model (2)" from_port="labelled data" to_op="Performance (2)" to_port="labelled data"/>
    <connect from_op="Performance (2)" from_port="performance" to_port="performance 1"/>
    <connect from_op="Performance (2)" from_port="example set" to_port="test set results"/>
    <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"/>
    <description align="left" color="blue" colored="true" height="103" resized="true" width="315" x="38" y="137">The model created in the Training step is applied to the current test set (10 %).&lt;br/&gt;The performance is evaluated and sent to the operator results.</description>
    <description align="center" color="transparent" colored="false" width="126">A cross-validation evaluating a decision tree model.</description>
    <operator activated="true" class="retrieve" compatibility="7.4.000" expanded="true" height="68" name="Retrieve Iris 10" width="90" x="45" y="187">
    <parameter key="repository_entry" value="Iris 10"/>
    <operator activated="true" class="discretize_by_bins" compatibility="7.4.000" expanded="true" height="103" name="Discretize (2)" width="90" x="179" y="187">
    <parameter key="number_of_bins" value="4"/>
    <operator activated="true" class="apply_model" compatibility="7.4.000" expanded="true" height="82" name="Apply Model" width="90" x="715" y="136">
    <list key="application_parameters"/>
    <connect from_op="Retrieve Iris Data" from_port="output" to_op="Discretize" to_port="example set input"/>
    <connect from_op="Discretize" from_port="example set output" to_op="Validation" to_port="example set"/>
    <connect from_op="Validation" from_port="model" to_op="Apply Model" to_port="model"/>
    <connect from_op="Validation" from_port="test result set" to_port="result 2"/>
    <connect from_op="Retrieve Iris 10" from_port="output" to_op="Discretize (2)" to_port="example set input"/>
    <connect from_op="Discretize (2)" from_port="example set output" to_op="Apply Model" to_port="unlabelled data"/>
    <connect from_op="Apply Model" from_port="labelled data" to_port="result 1"/>
    <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"/>
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