Random Forest
Flixport
New Altair Community Member
Hey RM Family,
I want to use the Random Forest here, as a result I get several trees displayed, understandable. But I saw in a tutorial that I can lead them to a result. Meaning, for example, I would need 80% training and 20% testing, so does the approach I brought here via the Split Data Operator work 20/80? I am looking forward to the answers
I want to use the Random Forest here, as a result I get several trees displayed, understandable. But I saw in a tutorial that I can lead them to a result. Meaning, for example, I would need 80% training and 20% testing, so does the approach I brought here via the Split Data Operator work 20/80? I am looking forward to the answers
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="9.2.001" 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="UTF-8"/>
<process expanded="true">
<operator activated="false" class="retrieve" compatibility="9.2.001" expanded="true" height="68" name="Retrieve data_out_select_by_pca_weights" width="90" x="45" y="85">
<parameter key="repository_entry" value="data/data_out_select_by_pca_weights"/>
</operator>
<operator activated="true" class="retrieve" compatibility="9.2.001" expanded="true" height="68" name="Retrieve data_out_select_by_chisq_weights" width="90" x="45" y="238">
<parameter key="repository_entry" value="data/data_out_select_by_chisq_weights"/>
</operator>
<operator activated="true" class="retrieve" compatibility="9.2.001" expanded="true" height="68" name="Retrieve data_out_pca" width="90" x="45" y="391">
<parameter key="repository_entry" value="data/data_out_pca"/>
</operator>
<operator activated="true" class="concurrency:join" compatibility="9.2.001" expanded="true" height="82" name="Join (2)" width="90" x="179" y="289">
<parameter key="remove_double_attributes" value="true"/>
<parameter key="join_type" value="inner"/>
<parameter key="use_id_attribute_as_key" value="true"/>
<list key="key_attributes"/>
<parameter key="keep_both_join_attributes" value="false"/>
</operator>
<operator activated="true" class="multiply" compatibility="9.2.001" expanded="true" height="103" name="Multiply" width="90" x="313" y="136"/>
<operator activated="true" class="filter_examples" compatibility="9.2.001" expanded="true" height="103" name="Filter Examples" width="90" x="514" y="85">
<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="Category.equals.financial"/>
<parameter key="filters_entry_key" value="Category.equals.political"/>
</list>
<parameter key="filters_logic_and" value="false"/>
<parameter key="filters_check_metadata" value="true"/>
</operator>
<operator activated="true" class="filter_examples" compatibility="9.2.001" expanded="true" height="103" name="Category == Economis" width="90" x="514" y="391">
<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="Category.equals.economic"/>
</list>
<parameter key="filters_logic_and" value="true"/>
<parameter key="filters_check_metadata" value="true"/>
</operator>
<operator activated="true" class="split_data" compatibility="9.2.001" expanded="true" height="82" name="20%" width="90" x="648" y="391">
<enumeration key="partitions">
<parameter key="ratio" value="0.25"/>
<parameter key="ratio" value="0.75"/>
</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="append" compatibility="9.2.001" expanded="true" height="103" name="Append" width="90" x="782" y="238">
<parameter key="datamanagement" value="double_array"/>
<parameter key="data_management" value="auto"/>
<parameter key="merge_type" value="all"/>
</operator>
<operator activated="true" class="split_data" compatibility="9.2.001" expanded="true" height="103" name="Split Data" width="90" x="916" y="289">
<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="x_validation" compatibility="5.1.002" expanded="true" height="124" name="Validation" width="90" x="1050" y="85">
<parameter key="create_complete_model" value="false"/>
<parameter key="average_performances_only" value="true"/>
<parameter key="leave_one_out" value="false"/>
<parameter key="number_of_validations" value="5"/>
<parameter key="sampling_type" value="2"/>
<parameter key="use_local_random_seed" value="false"/>
<parameter key="local_random_seed" value="1992"/>
<process expanded="true">
<operator activated="true" class="concurrency:parallel_random_forest" compatibility="9.2.001" expanded="true" height="103" name="Random Forest" width="90" x="179" y="34">
<parameter key="number_of_trees" value="100"/>
<parameter key="criterion" value="gain_ratio"/>
<parameter key="maximal_depth" value="10"/>
<parameter key="apply_pruning" value="false"/>
<parameter key="confidence" value="0.1"/>
<parameter key="apply_prepruning" value="false"/>
<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"/>
<parameter key="random_splits" value="false"/>
<parameter key="guess_subset_ratio" value="true"/>
<parameter key="subset_ratio" value="0.2"/>
<parameter key="voting_strategy" value="confidence vote"/>
<parameter key="use_local_random_seed" value="false"/>
<parameter key="local_random_seed" value="1992"/>
<parameter key="enable_parallel_execution" value="true"/>
</operator>
<connect from_port="training" to_op="Random Forest" to_port="training set"/>
<connect from_op="Random Forest" from_port="model" to_port="model"/>
<portSpacing port="source_training" 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="7.1.001" 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" compatibility="9.2.001" expanded="true" height="82" name="Performance" width="90" x="246" y="136">
<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="averagable 1"/>
<portSpacing port="source_model" spacing="0"/>
<portSpacing port="source_test set" spacing="0"/>
<portSpacing port="source_through 1" spacing="0"/>
<portSpacing port="sink_averagable 1" spacing="0"/>
<portSpacing port="sink_averagable 2" spacing="0"/>
</process>
</operator>
<operator activated="true" class="apply_model" compatibility="9.2.001" expanded="true" height="82" name="Apply Model (2)" width="90" x="1184" y="289">
<list key="application_parameters"/>
<parameter key="create_view" value="false"/>
</operator>
<operator activated="true" class="performance_classification" compatibility="9.2.001" expanded="true" height="82" name="Performance (2)" width="90" x="1318" y="391">
<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="true"/>
<parameter key="use_example_weights" value="true"/>
<list key="class_weights"/>
</operator>
<connect from_op="Retrieve data_out_select_by_chisq_weights" from_port="output" to_op="Join (2)" to_port="left"/>
<connect from_op="Retrieve data_out_pca" from_port="output" to_op="Join (2)" to_port="right"/>
<connect from_op="Join (2)" from_port="join" to_op="Multiply" to_port="input"/>
<connect from_op="Multiply" from_port="output 1" to_op="Filter Examples" to_port="example set input"/>
<connect from_op="Multiply" from_port="output 2" to_op="Category == Economis" to_port="example set input"/>
<connect from_op="Filter Examples" from_port="example set output" to_op="Append" to_port="example set 1"/>
<connect from_op="Category == Economis" from_port="example set output" to_op="20%" to_port="example set"/>
<connect from_op="20%" from_port="partition 1" to_op="Append" to_port="example set 2"/>
<connect from_op="Append" from_port="merged set" to_op="Split Data" to_port="example set"/>
<connect from_op="Split Data" from_port="partition 1" to_op="Validation" to_port="training"/>
<connect from_op="Split Data" from_port="partition 2" to_op="Apply Model (2)" to_port="unlabelled data"/>
<connect from_op="Validation" from_port="model" to_op="Apply Model (2)" to_port="model"/>
<connect from_op="Apply Model (2)" from_port="labelled data" to_op="Performance (2)" to_port="labelled data"/>
<connect from_op="Apply Model (2)" from_port="model" to_port="result 3"/>
<connect from_op="Performance (2)" from_port="performance" to_port="result 1"/>
<connect from_op="Performance (2)" from_port="example set" 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"/>
</process>
</operator>
</process>
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