NaN Values on Logistic Regression Output

B00100719
New Altair Community Member
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!
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!
Tagged:
0
Answers
-
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>0