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NaN Values on Logistic Regression Output
B00100719
Why would this be happening?
All numeric input - missing values are being replaced with averages before going into the LG operator
5 of my 25 inputs are returning NULL outputs?
I need the p-values
It's driving me nuts!
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B00100719
PROCESS.......
<?xml version="1.0" encoding="UTF-8"?><process version="9.3.001">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="9.3.001" 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="retrieve" compatibility="9.3.001" expanded="true" height="68" name="Retrieve ALL_KD_THESIS_WITH_WEIGHTED_CHR_35_MEASURES (2)" width="90" x="313" y="34">
<parameter key="repository_entry" value="../../data/ALL_KD_THESIS_WITH_WEIGHTED_CHR_35_MEASURES"/>
</operator>
<operator activated="true" class="replace_missing_values" compatibility="9.3.001" expanded="true" height="103" name="Replace Missing Values" width="90" x="514" y="34">
<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="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"/>
<parameter key="default" value="average"/>
<list key="columns"/>
<parameter key="replenishment_value" value="52"/>
</operator>
<operator activated="true" class="numerical_to_binominal" compatibility="9.3.001" expanded="true" height="82" name="Numerical to Binominal" width="90" x="648" y="34">
<parameter key="attribute_filter_type" value="single"/>
<parameter key="attribute" value="actual_ss_ct"/>
<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="true"/>
<parameter key="min" value="0.0"/>
<parameter key="max" value="0.0"/>
</operator>
<operator activated="true" class="set_role" compatibility="9.3.001" expanded="true" height="82" name="Set Role" width="90" x="782" y="34">
<parameter key="attribute_name" value="actual_ss_ct"/>
<parameter key="target_role" value="label"/>
<list key="set_additional_roles">
<parameter key="I_CLM" value="id"/>
<parameter key="actual_ss_ct" value="label"/>
</list>
</operator>
<operator activated="true" class="normalize" compatibility="9.3.001" expanded="true" height="103" name="Normalize" width="90" x="983" y="34">
<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="Z-transformation"/>
<parameter key="min" value="0.0"/>
<parameter key="max" value="1.0"/>
<parameter key="allow_negative_values" value="false"/>
</operator>
<operator activated="true" class="h2o:logistic_regression" compatibility="9.3.001" expanded="true" height="124" name="Logistic Regression (2)" width="90" x="1117" y="34">
<parameter key="solver" value="AUTO"/>
<parameter key="reproducible" value="false"/>
<parameter key="maximum_number_of_threads" value="4"/>
<parameter key="use_regularization" value="false"/>
<parameter key="lambda_search" value="false"/>
<parameter key="number_of_lambdas" value="0"/>
<parameter key="lambda_min_ratio" value="0.0"/>
<parameter key="early_stopping" value="true"/>
<parameter key="stopping_rounds" value="3"/>
<parameter key="stopping_tolerance" value="0.001"/>
<parameter key="standardize" value="true"/>
<parameter key="non-negative_coefficients" value="false"/>
<parameter key="add_intercept" value="true"/>
<parameter key="compute_p-values" value="true"/>
<parameter key="remove_collinear_columns" value="true"/>
<parameter key="missing_values_handling" value="MeanImputation"/>
<parameter key="max_iterations" value="1"/>
<parameter key="max_runtime_seconds" value="20"/>
</operator>
<connect from_op="Retrieve ALL_KD_THESIS_WITH_WEIGHTED_CHR_35_MEASURES (2)" from_port="output" to_op="Replace Missing Values" to_port="example set input"/>
<connect from_op="Replace Missing Values" from_port="example set output" to_op="Numerical to Binominal" to_port="example set input"/>
<connect from_op="Numerical to Binominal" from_port="example set output" to_op="Set Role" to_port="example set input"/>
<connect from_op="Set Role" from_port="example set output" to_op="Normalize" to_port="example set input"/>
<connect from_op="Normalize" from_port="example set output" to_op="Logistic Regression (2)" to_port="training set"/>
<connect from_op="Logistic Regression (2)" from_port="model" 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"/>
</process>
</operator>
</process>
sgenzer
B00100719
sorry no one has chimed in here. Is this still a problem?
Scott
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