Decision Tree Result not showing
When i run it, it's only showing perfomance vector, attribute weights, and example set, the decision tree result not showing. Before i'm using optimize selection (evolutionary), it worked just fine
here my model's screenshot
Here inside the optimize selection (evolutionary)
Here inside Split Validation
here my XML
here my model's screenshot
Here inside the optimize selection (evolutionary)
Here inside Split Validation
here my XML
<?xml version="1.0" encoding="UTF-8"?><process version="9.10.001">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="9.10.001" 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="read_csv" compatibility="9.10.001" expanded="true" height="68" name="Read CSV" width="90" x="112" y="34">
<parameter key="csv_file" value="D:\Kuliah\SKRIPSI\data.csv"/>
<parameter key="column_separators" value=";"/>
<parameter key="trim_lines" value="false"/>
<parameter key="use_quotes" value="true"/>
<parameter key="quotes_character" value="""/>
<parameter key="escape_character" value="\"/>
<parameter key="skip_comments" value="true"/>
<parameter key="comment_characters" value="#"/>
<parameter key="starting_row" value="1"/>
<parameter key="parse_numbers" value="true"/>
<parameter key="decimal_character" value="."/>
<parameter key="grouped_digits" value="false"/>
<parameter key="grouping_character" value=","/>
<parameter key="infinity_representation" value=""/>
<parameter key="date_format" value=""/>
<parameter key="first_row_as_names" value="true"/>
<list key="annotations"/>
<parameter key="time_zone" value="SYSTEM"/>
<parameter key="locale" value="English (United States)"/>
<parameter key="encoding" value="windows-1252"/>
<parameter key="read_all_values_as_polynominal" value="false"/>
<list key="data_set_meta_data_information">
<parameter key="0" value="Jenis Pengadaan.true.polynominal.attribute"/>
<parameter key="1" value="K/L/PD.true.polynominal.attribute"/>
<parameter key="2" value="HPS.true.integer.attribute"/>
<parameter key="3" value="Metode Pengadaan.true.polynominal.attribute"/>
<parameter key="4" value="Jenis Kualifikasi.true.polynominal.attribute"/>
<parameter key="5" value="Jenis Penilaian.true.polynominal.attribute"/>
<parameter key="6" value="Jenis Kontrak.true.polynominal.attribute"/>
<parameter key="7" value="Status.true.polynominal.attribute"/>
</list>
<parameter key="read_not_matching_values_as_missings" value="false"/>
</operator>
<operator activated="true" class="set_role" compatibility="9.10.001" expanded="true" height="82" name="Set Role" width="90" x="246" y="34">
<parameter key="attribute_name" value="Status"/>
<parameter key="target_role" value="label"/>
<list key="set_additional_roles"/>
</operator>
<operator activated="true" class="optimize_selection_evolutionary" compatibility="9.10.001" expanded="true" height="103" name="Optimize Selection (Evolutionary)" width="90" x="447" y="34">
<parameter key="use_exact_number_of_attributes" value="false"/>
<parameter key="restrict_maximum" value="false"/>
<parameter key="min_number_of_attributes" value="1"/>
<parameter key="max_number_of_attributes" value="1"/>
<parameter key="exact_number_of_attributes" value="1"/>
<parameter key="initialize_with_input_weights" value="false"/>
<parameter key="population_size" value="5"/>
<parameter key="maximum_number_of_generations" value="30"/>
<parameter key="use_early_stopping" value="false"/>
<parameter key="generations_without_improval" value="2"/>
<parameter key="normalize_weights" value="true"/>
<parameter key="use_local_random_seed" value="false"/>
<parameter key="local_random_seed" value="1992"/>
<parameter key="user_result_individual_selection" value="false"/>
<parameter key="show_population_plotter" value="false"/>
<parameter key="plot_generations" value="10"/>
<parameter key="constraint_draw_range" value="false"/>
<parameter key="draw_dominated_points" value="true"/>
<parameter key="maximal_fitness" value="Infinity"/>
<parameter key="selection_scheme" value="tournament"/>
<parameter key="tournament_size" value="0.25"/>
<parameter key="start_temperature" value="1.0"/>
<parameter key="dynamic_selection_pressure" value="true"/>
<parameter key="keep_best_individual" value="false"/>
<parameter key="save_intermediate_weights" value="false"/>
<parameter key="intermediate_weights_generations" value="10"/>
<parameter key="p_initialize" value="0.5"/>
<parameter key="p_mutation" value="-1.0"/>
<parameter key="p_crossover" value="0.5"/>
<parameter key="crossover_type" value="uniform"/>
<process expanded="true">
<operator activated="true" class="split_validation" compatibility="9.10.001" expanded="true" height="124" name="Validation" width="90" x="313" y="85">
<parameter key="create_complete_model" value="false"/>
<parameter key="split" value="relative"/>
<parameter key="split_ratio" value="0.7"/>
<parameter key="training_set_size" value="100"/>
<parameter key="test_set_size" value="-1"/>
<parameter key="sampling_type" value="automatic"/>
<parameter key="use_local_random_seed" value="false"/>
<parameter key="local_random_seed" value="1992"/>
<process expanded="true">
<operator activated="true" class="concurrency:parallel_decision_tree" compatibility="9.10.001" expanded="true" height="103" name="Decision Tree" width="90" x="179" y="34">
<parameter key="criterion" value="gain_ratio"/>
<parameter key="maximal_depth" value="10"/>
<parameter key="apply_pruning" value="true"/>
<parameter key="confidence" value="0.1"/>
<parameter key="apply_prepruning" value="true"/>
<parameter key="minimal_gain" value="0.01"/>
<parameter key="minimal_leaf_size" value="2"/>
<parameter key="minimal_size_for_split" value="4"/>
<parameter key="number_of_prepruning_alternatives" value="3"/>
</operator>
<connect from_port="training" to_op="Decision Tree" to_port="training set"/>
<connect from_op="Decision Tree" from_port="model" to_port="model"/>
<connect from_op="Decision Tree" from_port="exampleSet" to_port="through 1"/>
<portSpacing port="source_training" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
<portSpacing port="sink_through 1" spacing="0"/>
<portSpacing port="sink_through 2" spacing="0"/>
</process>
<process expanded="true">
<operator activated="true" class="apply_model" compatibility="9.10.001" expanded="true" height="82" name="Apply Model" width="90" x="112" y="34">
<list key="application_parameters"/>
<parameter key="create_view" value="false"/>
</operator>
<operator activated="true" class="performance_classification" compatibility="9.10.001" expanded="true" height="82" name="Performance" width="90" x="246" y="34">
<parameter key="main_criterion" value="first"/>
<parameter key="accuracy" value="true"/>
<parameter key="classification_error" value="false"/>
<parameter key="kappa" value="false"/>
<parameter key="weighted_mean_recall" value="false"/>
<parameter key="weighted_mean_precision" value="false"/>
<parameter key="spearman_rho" value="false"/>
<parameter key="kendall_tau" value="false"/>
<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_mean_squared_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="cross-entropy" value="false"/>
<parameter key="margin" value="false"/>
<parameter key="soft_margin_loss" value="false"/>
<parameter key="logistic_loss" value="false"/>
<parameter key="skip_undefined_labels" value="false"/>
<parameter key="use_example_weights" value="true"/>
<list key="class_weights"/>
</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="averagable 1"/>
<portSpacing port="source_model" spacing="0"/>
<portSpacing port="source_test set" spacing="0"/>
<portSpacing port="source_through 1" spacing="0"/>
<portSpacing port="source_through 2" spacing="0"/>
<portSpacing port="sink_averagable 1" spacing="0"/>
<portSpacing port="sink_averagable 2" spacing="0"/>
</process>
</operator>
<connect from_port="example set" to_op="Validation" to_port="training"/>
<connect from_op="Validation" from_port="averagable 1" to_port="performance"/>
<portSpacing port="source_example set" spacing="0"/>
<portSpacing port="source_through 1" spacing="0"/>
<portSpacing port="sink_performance" spacing="0"/>
</process>
</operator>
<connect from_op="Read CSV" from_port="output" to_op="Set Role" to_port="example set input"/>
<connect from_op="Set Role" from_port="example set output" to_op="Optimize Selection (Evolutionary)" to_port="example set in"/>
<connect from_op="Optimize Selection (Evolutionary)" from_port="example set out" to_port="result 1"/>
<connect from_op="Optimize Selection (Evolutionary)" from_port="weights" to_port="result 2"/>
<connect from_op="Optimize Selection (Evolutionary)" from_port="performance" to_port="result 3"/>
<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"/>
</process>
</operator>
</process>
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Hi,
The operator you are using will give you the most important features for what you want to use the data for; classification, regression, etc. So you have to use the new dataset at the output to do your prediction with the Decision Tree.
Or you can use the "Optimize Parameters (Evolutionary)" under Modeling > Optimization > Parameters, that will give the tree model in addition. I hope will help somehow.
Regards.
The operator you are using will give you the most important features for what you want to use the data for; classification, regression, etc. So you have to use the new dataset at the output to do your prediction with the Decision Tree.
Or you can use the "Optimize Parameters (Evolutionary)" under Modeling > Optimization > Parameters, that will give the tree model in addition. I hope will help somehow.
Regards.
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1 - 1 of
11
Hi!
The feature selection is not supposed to show you a model. It tries a lot of different models.
Just put a Decision Tree after the selection, it will be built on the selected attributes.
The best approach is to put the feature selection into a cross validation (yes, then you'll have an "outer" and an "inner" validation). You will get a good estimation for the accuracy of the entire modeling process that way.
Inside the cross validation:
Left: Feature selection (evolutionary), Decision Tree
Right: Apply Model, Performance
Regards,
Balázs
The feature selection is not supposed to show you a model. It tries a lot of different models.
Just put a Decision Tree after the selection, it will be built on the selected attributes.
The best approach is to put the feature selection into a cross validation (yes, then you'll have an "outer" and an "inner" validation). You will get a good estimation for the accuracy of the entire modeling process that way.
Inside the cross validation:
Left: Feature selection (evolutionary), Decision Tree
Right: Apply Model, Performance
Regards,
Balázs
The feature selection is not supposed to show you a model. It tries a lot of different models.
Just put a Decision Tree after the selection, it will be built on the selected attributes.
The best approach is to put the feature selection into a cross validation (yes, then you'll have an "outer" and an "inner" validation). You will get a good estimation for the accuracy of the entire modeling process that way.
Inside the cross validation:
Left: Feature selection (evolutionary), Decision Tree
Right: Apply Model, Performance
Regards,
Balázs