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Is my model good enough?

oneponep Member Posts: 20  Maven
edited November 2018 in Help

Hello.

I'm trying to make a predictive regression model, but having a hard time telling if my model is good enough?

I'm using X-validation and I've read somewhere that you can tell if it's a good fit based on the difference between the training error and the validation error? But how do I get the X-validation to tell me the training error?

 

Currently my model has a RMSE of about 1,000 and my label has a range from 0 to about 32,000. Out from this I can't really tell if it is any good? Is there another way I can measure if it's a good model?

 

Oh, and one more thing - I can manage to make my model better if I use a k-NN global anomaly score and remove some of the outliers comming from noise - but i'm afraid that I remove too much information. How can I decide how many outliers I can remove?

 

Thanks in advance!

Answers

  • mschmitzmschmitz Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,158  RM Data Scientist

    Hi mathias,

    it really depends on the use case wether this is good or not. Hard to judge. But i would of course look at the testing error, not the training.

     

    What might help you to get a better feeling is to get a plot of the scored set returned by the new Cross Validation in 7.3. Just plot label against prediction(label) in a scatter plot. You can extract information like "if the truth is 5, my prediction is between 3 and 7".

     

    Best,

    Martin

    - Head of Data Science Services at RapidMiner -
    Dortmund, Germany
  • oneponep Member Posts: 20  Maven

    Okay, I've tried to do that.

     

    I figured out if I use the k-NN Global Anomaly and filter out some outliers, I can get the RMSE lower - is that ok to do?

  • oneponep Member Posts: 20  Maven

    Here is my process if you like to look it over;

     

    <?xml version="1.0" encoding="UTF-8"?><process version="7.3.000">
    <context>
    <input/>
    <output/>
    <macros/>
    </context>
    <operator activated="true" class="process" compatibility="7.3.000" expanded="true" name="Process">
    <parameter key="logverbosity" value="init"/>
    <parameter key="random_seed" value="2001"/>
    <parameter key="send_mail" value="never"/>
    <parameter key="notification_email" value=""/>
    <parameter key="process_duration_for_mail" value="30"/>
    <parameter key="encoding" value="SYSTEM"/>
    <process expanded="true">
    <operator activated="true" class="retrieve" compatibility="7.3.000" expanded="true" height="68" name="Retrieve data-windows-avg" width="90" x="45" y="187">
    <parameter key="repository_entry" value="//iMac/data-windows-avg"/>
    </operator>
    <operator activated="true" class="set_role" compatibility="7.3.000" expanded="true" height="82" name="Set Role" width="90" x="179" y="187">
    <parameter key="attribute_name" value="Shaftpower (avg)"/>
    <parameter key="target_role" value="label"/>
    <list key="set_additional_roles"/>
    </operator>
    <operator activated="true" class="select_attributes" compatibility="7.3.000" expanded="true" height="82" name="Select Attributes" width="90" x="313" y="187">
    <parameter key="attribute_filter_type" value="subset"/>
    <parameter key="attribute" value=""/>
    <parameter key="attributes" value="Shaftpower (avg)|1212001PIT (avg)|1212001PIT (coe)|1212001PIT (var)|1212001ROL (avg)|1212001ROL (coe)|1212001ROL (var)|1215001SI3 (avg)|1215001SI3 (coe)|1215001SI3 (var)|1223001ZT1_Angle (avg)|1223001ZT1_Angle (coe)|1223001ZT1_Angle (var)|1223001ZT2_Angle (avg)|1223001ZT2_Angle (coe)|1223001ZT2_Angle (var)|1225001PS_crosswind (avg)|1225001PS_crosswind (coe)|1225001PS_crosswind (var)|1225001PS_headwind (avg)|1225001PS_headwind (coe)|1225001PS_headwind (var)|1907001ZT_Lin (avg)|1907001ZT_Lin (coe)|1907001ZT_Lin (var)|1907002ZT_Lin (avg)|1907002ZT_Lin (coe)|1907002ZT_Lin (var)|1225001DFTM (var)|1225001DFTM (coe)|1225001DFTM (avg)|1212001ROLR (var)|1212001ROLR (coe)|1212001ROLR (avg)|1212001PITR (var)|1212001PITR (coe)|1212001PITR (avg)|1212001HEV (var)|1212001HEV (coe)|1212001HEV (avg)|0603703ZT2 (var)|0603703ZT2 (coe)|0603703ZT2 (avg)|0603703ZT1 (var)|0603703ZT1 (coe)|0603703ZT1 (avg)"/>
    <parameter key="use_except_expression" value="false"/>
    <parameter key="value_type" value="attribute_value"/>
    <parameter key="use_value_type_exception" value="false"/>
    <parameter key="except_value_type" value="time"/>
    <parameter key="block_type" value="attribute_block"/>
    <parameter key="use_block_type_exception" value="false"/>
    <parameter key="except_block_type" value="value_matrix_row_start"/>
    <parameter key="invert_selection" value="false"/>
    <parameter key="include_special_attributes" value="false"/>
    </operator>
    <operator activated="true" class="filter_examples" compatibility="7.3.000" expanded="true" height="103" name="Filter Examples" width="90" x="447" y="187">
    <parameter key="parameter_expression" value=""/>
    <parameter key="condition_class" value="no_missing_attributes"/>
    <parameter key="invert_filter" value="false"/>
    <list key="filters_list"/>
    <parameter key="filters_logic_and" value="true"/>
    <parameter key="filters_check_metadata" value="true"/>
    </operator>
    <operator activated="true" class="anomalydetection:k-NN Global Anomaly Score" compatibility="2.3.002" expanded="true" height="103" name="k-NN Global Anomaly Score" width="90" x="45" y="442">
    <parameter key="k" value="1"/>
    <parameter key="use k-th neighbor distance only (no average)" value="false"/>
    <parameter key="measure_types" value="MixedMeasures"/>
    <parameter key="mixed_measure" value="MixedEuclideanDistance"/>
    <parameter key="nominal_measure" value="NominalDistance"/>
    <parameter key="numerical_measure" value="EuclideanDistance"/>
    <parameter key="divergence" value="GeneralizedIDivergence"/>
    <parameter key="kernel_type" value="radial"/>
    <parameter key="kernel_gamma" value="1.0"/>
    <parameter key="kernel_sigma1" value="1.0"/>
    <parameter key="kernel_sigma2" value="0.0"/>
    <parameter key="kernel_sigma3" value="2.0"/>
    <parameter key="kernel_degree" value="3.0"/>
    <parameter key="kernel_shift" value="1.0"/>
    <parameter key="kernel_a" value="1.0"/>
    <parameter key="kernel_b" value="0.0"/>
    <parameter key="parallelize evaluation process" value="false"/>
    <parameter key="number of threads" value="4"/>
    </operator>
    <operator activated="true" class="filter_examples" compatibility="7.3.000" expanded="true" height="103" name="Filter Examples (2)" width="90" x="179" y="442">
    <parameter key="parameter_expression" value=""/>
    <parameter key="condition_class" value="custom_filters"/>
    <parameter key="invert_filter" value="false"/>
    <list key="filters_list">
    <parameter key="filters_entry_key" value="outlier.le.5"/>
    </list>
    <parameter key="filters_logic_and" value="true"/>
    <parameter key="filters_check_metadata" value="true"/>
    </operator>
    <operator activated="true" class="normalize" compatibility="7.3.000" expanded="true" height="103" name="Normalize" width="90" x="313" y="442">
    <parameter key="return_preprocessing_model" value="false"/>
    <parameter key="create_view" value="false"/>
    <parameter key="attribute_filter_type" value="all"/>
    <parameter key="attribute" value=""/>
    <parameter key="attributes" value=""/>
    <parameter key="use_except_expression" value="false"/>
    <parameter key="value_type" value="numeric"/>
    <parameter key="use_value_type_exception" value="false"/>
    <parameter key="except_value_type" value="real"/>
    <parameter key="block_type" value="value_series"/>
    <parameter key="use_block_type_exception" value="false"/>
    <parameter key="except_block_type" value="value_series_end"/>
    <parameter key="invert_selection" value="false"/>
    <parameter key="include_special_attributes" value="false"/>
    <parameter key="method" value="range transformation"/>
    <parameter key="min" value="0.0"/>
    <parameter key="max" value="1.0"/>
    </operator>
    <operator activated="true" class="weight_by_correlation" compatibility="7.3.000" expanded="true" height="82" name="Weight by Correlation" width="90" x="447" y="442">
    <parameter key="normalize_weights" value="false"/>
    <parameter key="sort_weights" value="true"/>
    <parameter key="sort_direction" value="ascending"/>
    <parameter key="squared_correlation" value="false"/>
    </operator>
    <operator activated="true" class="scale_by_weights" compatibility="7.3.000" expanded="true" height="82" name="Scale by Weights" width="90" x="581" y="442"/>
    <operator activated="true" class="split_data" compatibility="7.3.000" expanded="true" height="103" name="Split Data" width="90" x="715" y="442">
    <enumeration key="partitions">
    <parameter key="ratio" value="0.8"/>
    <parameter key="ratio" value="0.2"/>
    </enumeration>
    <parameter key="sampling_type" value="shuffled sampling"/>
    <parameter key="use_local_random_seed" value="false"/>
    <parameter key="local_random_seed" value="1992"/>
    </operator>
    <operator activated="true" class="concurrency:cross_validation" compatibility="7.3.000" expanded="true" height="145" name="Cross Validation" width="90" x="782" y="187">
    <parameter key="split_on_batch_attribute" value="false"/>
    <parameter key="leave_one_out" value="false"/>
    <parameter key="number_of_folds" value="10"/>
    <parameter key="sampling_type" value="automatic"/>
    <parameter key="use_local_random_seed" value="false"/>
    <parameter key="local_random_seed" value="1992"/>
    <parameter key="enable_parallel_execution" value="true"/>
    <process expanded="true">
    <operator activated="true" class="k_nn" compatibility="7.3.000" expanded="true" height="82" name="k-NN" width="90" x="380" y="34">
    <parameter key="k" value="1"/>
    <parameter key="weighted_vote" value="false"/>
    <parameter key="measure_types" value="MixedMeasures"/>
    <parameter key="mixed_measure" value="MixedEuclideanDistance"/>
    <parameter key="nominal_measure" value="NominalDistance"/>
    <parameter key="numerical_measure" value="EuclideanDistance"/>
    <parameter key="divergence" value="GeneralizedIDivergence"/>
    <parameter key="kernel_type" value="radial"/>
    <parameter key="kernel_gamma" value="1.0"/>
    <parameter key="kernel_sigma1" value="1.0"/>
    <parameter key="kernel_sigma2" value="0.0"/>
    <parameter key="kernel_sigma3" value="2.0"/>
    <parameter key="kernel_degree" value="3.0"/>
    <parameter key="kernel_shift" value="1.0"/>
    <parameter key="kernel_a" value="1.0"/>
    <parameter key="kernel_b" value="0.0"/>
    </operator>
    <connect from_port="training set" to_op="k-NN" to_port="training set"/>
    <connect from_op="k-NN" 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="7.3.000" expanded="true" height="82" name="Apply Model (3)" width="90" x="179" y="187">
    <list key="application_parameters"/>
    <parameter key="create_view" value="false"/>
    </operator>
    <operator activated="true" class="performance_regression" compatibility="7.3.000" expanded="true" height="82" name="Performance (3)" width="90" x="313" y="187">
    <parameter key="main_criterion" value="first"/>
    <parameter key="root_mean_squared_error" value="true"/>
    <parameter key="absolute_error" value="false"/>
    <parameter key="relative_error" value="false"/>
    <parameter key="relative_error_lenient" value="false"/>
    <parameter key="relative_error_strict" value="false"/>
    <parameter key="normalized_absolute_error" value="false"/>
    <parameter key="root_relative_squared_error" value="false"/>
    <parameter key="squared_error" value="false"/>
    <parameter key="correlation" value="false"/>
    <parameter key="squared_correlation" value="false"/>
    <parameter key="prediction_average" value="false"/>
    <parameter key="spearman_rho" value="false"/>
    <parameter key="kendall_tau" 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 (3)" to_port="model"/>
    <connect from_port="test set" to_op="Apply Model (3)" to_port="unlabelled data"/>
    <connect from_op="Apply Model (3)" from_port="labelled data" to_op="Performance (3)" to_port="labelled data"/>
    <connect from_op="Performance (3)" from_port="performance" to_port="performance 1"/>
    <connect from_op="Performance (3)" 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"/>
    </process>
    </operator>
    <operator activated="true" class="apply_model" compatibility="7.3.000" expanded="true" height="82" name="Apply Model (2)" width="90" x="916" y="544">
    <list key="application_parameters"/>
    <parameter key="create_view" value="false"/>
    </operator>
    <operator activated="true" class="performance_regression" compatibility="7.3.000" expanded="true" height="82" name="Performance (2)" width="90" x="1050" y="544">
    <parameter key="main_criterion" value="root_mean_squared_error"/>
    <parameter key="root_mean_squared_error" value="true"/>
    <parameter key="absolute_error" value="true"/>
    <parameter key="relative_error" value="true"/>
    <parameter key="relative_error_lenient" value="true"/>
    <parameter key="relative_error_strict" value="true"/>
    <parameter key="normalized_absolute_error" value="true"/>
    <parameter key="root_relative_squared_error" value="true"/>
    <parameter key="squared_error" value="true"/>
    <parameter key="correlation" value="true"/>
    <parameter key="squared_correlation" value="true"/>
    <parameter key="prediction_average" value="true"/>
    <parameter key="spearman_rho" value="true"/>
    <parameter key="kendall_tau" value="true"/>
    <parameter key="skip_undefined_labels" value="true"/>
    <parameter key="use_example_weights" value="true"/>
    </operator>
    <operator activated="true" class="generate_attributes" compatibility="7.3.000" expanded="true" height="82" name="Generate Attributes" width="90" x="1184" y="595">
    <list key="function_descriptions">
    <parameter key="Diff" value="[Shaftpower (avg)]-[prediction(Shaftpower (avg))]"/>
    </list>
    <parameter key="keep_all" value="true"/>
    </operator>
    <operator activated="true" class="generate_attributes" compatibility="7.3.000" expanded="true" height="82" name="Generate Attributes (2)" width="90" x="1318" y="595">
    <list key="function_descriptions">
    <parameter key="Accuracy" value="100-((abs(Diff)/[Shaftpower (avg)])*100)"/>
    </list>
    <parameter key="keep_all" value="true"/>
    </operator>
    <connect from_op="Retrieve data-windows-avg" from_port="output" to_op="Set Role" to_port="example set input"/>
    <connect from_op="Set Role" from_port="example set output" to_op="Select Attributes" to_port="example set input"/>
    <connect from_op="Select Attributes" from_port="example set output" to_op="Filter Examples" to_port="example set input"/>
    <connect from_op="Filter Examples" from_port="example set output" to_op="k-NN Global Anomaly Score" to_port="example set"/>
    <connect from_op="k-NN Global Anomaly Score" from_port="example set" to_op="Filter Examples (2)" to_port="example set input"/>
    <connect from_op="Filter Examples (2)" from_port="example set output" to_op="Normalize" to_port="example set input"/>
    <connect from_op="Normalize" from_port="example set output" to_op="Weight by Correlation" to_port="example set"/>
    <connect from_op="Weight by Correlation" from_port="weights" to_op="Scale by Weights" to_port="weights"/>
    <connect from_op="Weight by Correlation" from_port="example set" to_op="Scale by Weights" to_port="example set"/>
    <connect from_op="Scale by Weights" from_port="example set" to_op="Split Data" to_port="example set"/>
    <connect from_op="Split Data" from_port="partition 1" to_op="Cross Validation" to_port="example set"/>
    <connect from_op="Split Data" from_port="partition 2" to_op="Apply Model (2)" to_port="unlabelled data"/>
    <connect from_op="Cross Validation" from_port="model" to_op="Apply Model (2)" to_port="model"/>
    <connect from_op="Cross Validation" from_port="test result set" to_port="result 3"/>
    <connect from_op="Cross Validation" from_port="performance 1" to_port="result 4"/>
    <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="result 1"/>
    <connect from_op="Performance (2)" from_port="example set" to_op="Generate Attributes" to_port="example set input"/>
    <connect from_op="Generate Attributes" from_port="example set output" to_op="Generate Attributes (2)" to_port="example set input"/>
    <connect from_op="Generate Attributes (2)" from_port="example set output" 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"/>
    <portSpacing port="sink_result 4" spacing="0"/>
    <portSpacing port="sink_result 5" spacing="0"/>
    </process>
    </operator>
    </process>
  • mschmitzmschmitz Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,158  RM Data Scientist

    Mathias,

     

    i do think it makes some sense to filter out the outlier - it often makes models better. The downside of this, is that your model does not cover examples with a high outlier score. I would argue that you want to do it anyway, because you cannot find good statistical reasoning for these outliers.

     

    ~Martin

    - Head of Data Science Services at RapidMiner -
    Dortmund, Germany
  • oneponep Member Posts: 20  Maven

    Hello again.

    I have tried to remove the outliers, but it turns out that it doesn't really have an effect on my RMSE.

     

    I'm having a hard time telling how I could improve my model - as of right now I get a RMSE around 850, and I would like it to be atleast half of that. Could someone tell me what i'm doing wrong?

     

    Here is my process;

    <?xml version="1.0" encoding="UTF-8"?><process version="7.3.000">
    <context>
    <input/>
    <output/>
    <macros/>
    </context>
    <operator activated="true" class="process" compatibility="7.3.000" expanded="true" name="Process">
    <parameter key="logverbosity" value="init"/>
    <parameter key="random_seed" value="2001"/>
    <parameter key="send_mail" value="never"/>
    <parameter key="notification_email" value=""/>
    <parameter key="process_duration_for_mail" value="30"/>
    <parameter key="encoding" value="SYSTEM"/>
    <process expanded="true">
    <operator activated="true" class="retrieve" compatibility="7.3.000" expanded="true" height="68" name="Retrieve traindata" width="90" x="45" y="187">
    <parameter key="repository_entry" value="//Local Repository/traindata"/>
    </operator>
    <operator activated="true" class="set_role" compatibility="7.3.000" expanded="true" height="82" name="Set Role" width="90" x="179" y="187">
    <parameter key="attribute_name" value="Shaftpower (avg)"/>
    <parameter key="target_role" value="label"/>
    <list key="set_additional_roles"/>
    </operator>
    <operator activated="true" class="select_attributes" compatibility="7.3.000" expanded="true" height="82" name="Select Attributes" width="90" x="313" y="187">
    <parameter key="attribute_filter_type" value="subset"/>
    <parameter key="attribute" value=""/>
    <parameter key="attributes" value="0603703ZT1 (avg)|0603703ZT1 (coe)|0603703ZT1 (var)|0603703ZT2 (avg)|0603703ZT2 (coe)|0603703ZT2 (var)|1212001HEV (avg)|1212001HEV (coe)|1212001HEV (var)|1212001PIT (avg)|1212001PIT (coe)|1212001PIT (var)|1212001PITR (avg)|1212001PITR (coe)|1212001PITR (var)|1212001ROL (avg)|1212001ROL (coe)|1212001ROL (var)|1212001ROLR (avg)|1212001ROLR (coe)|1212001ROLR (var)|1215001SI3 (avg)|1215001SI3 (coe)|1215001SI3 (var)|1223001ZT1_Angle (avg)|1223001ZT1_Angle (coe)|1223001ZT1_Angle (var)|1223001ZT2_Angle (avg)|1223001ZT2_Angle (coe)|1223001ZT2_Angle (var)|1225001DFTM (avg)|1225001DFTM (coe)|1225001DFTM (var)|1225001PS_crosswind (avg)|1225001PS_crosswind (coe)|1225001PS_crosswind (var)|1225001PS_headwind (avg)|1225001PS_headwind (coe)|1225001PS_headwind (var)|1907001ZT_Lin (avg)|1907001ZT_Lin (coe)|1907001ZT_Lin (var)|1907002ZT_Lin (avg)|1907002ZT_Lin (coe)|1907002ZT_Lin (var)|Shaftpower (avg)|1215001SI2 (var)|1215001SI2 (coe)|1215001SI2 (avg)|1215001SI1 (var)|1215001SI1 (coe)|1215001SI1 (avg)"/>
    <parameter key="use_except_expression" value="false"/>
    <parameter key="value_type" value="attribute_value"/>
    <parameter key="use_value_type_exception" value="false"/>
    <parameter key="except_value_type" value="time"/>
    <parameter key="block_type" value="attribute_block"/>
    <parameter key="use_block_type_exception" value="false"/>
    <parameter key="except_block_type" value="value_matrix_row_start"/>
    <parameter key="invert_selection" value="false"/>
    <parameter key="include_special_attributes" value="false"/>
    </operator>
    <operator activated="true" class="filter_examples" compatibility="7.3.000" expanded="true" height="103" name="Filter Examples" width="90" x="447" y="187">
    <parameter key="parameter_expression" value=""/>
    <parameter key="condition_class" value="no_missing_attributes"/>
    <parameter key="invert_filter" value="false"/>
    <list key="filters_list"/>
    <parameter key="filters_logic_and" value="true"/>
    <parameter key="filters_check_metadata" value="true"/>
    </operator>
    <operator activated="true" class="filter_examples" compatibility="7.3.000" expanded="true" height="103" name="Filter Examples (2)" width="90" x="581" y="187">
    <parameter key="parameter_expression" value=""/>
    <parameter key="condition_class" value="custom_filters"/>
    <parameter key="invert_filter" value="false"/>
    <list key="filters_list">
    <parameter key="filters_entry_key" value="1215001SI3 (avg).ge.0\.1"/>
    </list>
    <parameter key="filters_logic_and" value="true"/>
    <parameter key="filters_check_metadata" value="true"/>
    </operator>
    <operator activated="true" class="normalize" compatibility="7.3.000" expanded="true" height="103" name="Normalize" width="90" x="313" y="595">
    <parameter key="return_preprocessing_model" value="false"/>
    <parameter key="create_view" value="false"/>
    <parameter key="attribute_filter_type" value="all"/>
    <parameter key="attribute" value=""/>
    <parameter key="attributes" value=""/>
    <parameter key="use_except_expression" value="false"/>
    <parameter key="value_type" value="numeric"/>
    <parameter key="use_value_type_exception" value="false"/>
    <parameter key="except_value_type" value="real"/>
    <parameter key="block_type" value="value_series"/>
    <parameter key="use_block_type_exception" value="false"/>
    <parameter key="except_block_type" value="value_series_end"/>
    <parameter key="invert_selection" value="false"/>
    <parameter key="include_special_attributes" value="false"/>
    <parameter key="method" value="range transformation"/>
    <parameter key="min" value="0.0"/>
    <parameter key="max" value="1.0"/>
    </operator>
    <operator activated="true" class="weight_by_correlation" compatibility="7.3.000" expanded="true" height="82" name="Weight by Correlation" width="90" x="447" y="595">
    <parameter key="normalize_weights" value="false"/>
    <parameter key="sort_weights" value="true"/>
    <parameter key="sort_direction" value="ascending"/>
    <parameter key="squared_correlation" value="false"/>
    </operator>
    <operator activated="true" class="scale_by_weights" compatibility="7.3.000" expanded="true" height="82" name="Scale by Weights" width="90" x="581" y="595"/>
    <operator activated="true" class="concurrency:cross_validation" compatibility="7.3.000" expanded="true" height="145" name="Cross Validation" width="90" x="715" y="595">
    <parameter key="split_on_batch_attribute" value="false"/>
    <parameter key="leave_one_out" value="false"/>
    <parameter key="number_of_folds" value="10"/>
    <parameter key="sampling_type" value="automatic"/>
    <parameter key="use_local_random_seed" value="false"/>
    <parameter key="local_random_seed" value="1992"/>
    <parameter key="enable_parallel_execution" value="true"/>
    <process expanded="true">
    <operator activated="true" class="k_nn" compatibility="7.3.000" expanded="true" height="82" name="k-NN" width="90" x="179" y="34">
    <parameter key="k" value="2"/>
    <parameter key="weighted_vote" value="false"/>
    <parameter key="measure_types" value="NumericalMeasures"/>
    <parameter key="mixed_measure" value="MixedEuclideanDistance"/>
    <parameter key="nominal_measure" value="NominalDistance"/>
    <parameter key="numerical_measure" value="ManhattanDistance"/>
    <parameter key="divergence" value="GeneralizedIDivergence"/>
    <parameter key="kernel_type" value="radial"/>
    <parameter key="kernel_gamma" value="1.0"/>
    <parameter key="kernel_sigma1" value="1.0"/>
    <parameter key="kernel_sigma2" value="0.0"/>
    <parameter key="kernel_sigma3" value="2.0"/>
    <parameter key="kernel_degree" value="3.0"/>
    <parameter key="kernel_shift" value="1.0"/>
    <parameter key="kernel_a" value="1.0"/>
    <parameter key="kernel_b" value="0.0"/>
    </operator>
    <connect from_port="training set" to_op="k-NN" to_port="training set"/>
    <connect from_op="k-NN" 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="7.3.000" expanded="true" height="82" name="Apply Model (3)" width="90" x="179" y="187">
    <list key="application_parameters"/>
    <parameter key="create_view" value="false"/>
    </operator>
    <operator activated="true" class="performance_regression" compatibility="7.3.000" expanded="true" height="82" name="Performance" width="90" x="380" y="187">
    <parameter key="main_criterion" value="root_mean_squared_error"/>
    <parameter key="root_mean_squared_error" value="true"/>
    <parameter key="absolute_error" value="true"/>
    <parameter key="relative_error" value="true"/>
    <parameter key="relative_error_lenient" value="true"/>
    <parameter key="relative_error_strict" value="true"/>
    <parameter key="normalized_absolute_error" value="true"/>
    <parameter key="root_relative_squared_error" value="true"/>
    <parameter key="squared_error" value="true"/>
    <parameter key="correlation" value="true"/>
    <parameter key="squared_correlation" value="true"/>
    <parameter key="prediction_average" value="true"/>
    <parameter key="spearman_rho" value="true"/>
    <parameter key="kendall_tau" value="true"/>
    <parameter key="skip_undefined_labels" value="true"/>
    <parameter key="use_example_weights" value="true"/>
    </operator>
    <connect from_port="model" to_op="Apply Model (3)" to_port="model"/>
    <connect from_port="test set" to_op="Apply Model (3)" to_port="unlabelled data"/>
    <connect from_op="Apply Model (3)" from_port="labelled data" to_op="Performance" to_port="labelled data"/>
    <connect from_op="Performance" from_port="performance" to_port="performance 1"/>
    <connect from_op="Performance" 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"/>
    </process>
    </operator>
    <connect from_op="Retrieve traindata" from_port="output" to_op="Set Role" to_port="example set input"/>
    <connect from_op="Set Role" from_port="example set output" to_op="Select Attributes" to_port="example set input"/>
    <connect from_op="Select Attributes" from_port="example set output" to_op="Filter Examples" to_port="example set input"/>
    <connect from_op="Filter Examples" from_port="example set output" to_op="Filter Examples (2)" to_port="example set input"/>
    <connect from_op="Filter Examples (2)" from_port="example set output" to_op="Normalize" to_port="example set input"/>
    <connect from_op="Normalize" from_port="example set output" to_op="Weight by Correlation" to_port="example set"/>
    <connect from_op="Weight by Correlation" from_port="weights" to_op="Scale by Weights" to_port="weights"/>
    <connect from_op="Weight by Correlation" from_port="example set" to_op="Scale by Weights" to_port="example set"/>
    <connect from_op="Scale by Weights" from_port="example set" to_op="Cross Validation" to_port="example set"/>
    <connect from_op="Cross Validation" from_port="test result set" to_port="result 2"/>
    <connect from_op="Cross Validation" from_port="performance 1" 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"/>
    </process>
    </operator>
    </process>

    And here is my data;

    https://www.dropbox.com/s/w9a5545nn1vs0b8/traindata.csv?dl=0

     

    I'm trying to predict the shaftpower for a ship.

     

    Thank you in advance!

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