"[SOLVED] Confidence for Regression Problems"
Stefan
New Altair Community Member
Hi,
I have a basic regression problem with several numeric attributes. First, I perform feature subset selection, then model training, and then prediction on test data. By the way, it is awesome, that RapidMiner let's me do all those things without hassle. However, I encountered some situations where I would like to get more information regarding the stability of the methods I used.
I have two questions:
(1) Is it possible to get variance from "Loop and Average" regarding the averagable in addition to mean? This works for performance vectors, but not weight vectors.
I built a module for feature subset selection which returns the best subset. It is represented by an attribute weight vector, i.e. 0 = not selected, 1 = selected. This process has a random component and I repeat it with "Loop and Average" to get different random seeds, and average the selection vectors. The result should be a table of each attribute and their relative selection count with mean and variance. However, I only get mean. I tried a workaround with "Log". I can get the performances for each cross-validation fold, and also each cross-validation itself using "applycount" as primary and foreign keys. However, I cannot get any information from the "Optimize Selection" operator, e.g. "feature_names" always delivers "?". Please, have a look at the logs in the example.
I built a module for model training and prediction. Training is basically bagging applied to the induction algorithm. Then, I use "Apply Model" for prediction which should internally apply each bag model to the sample and compute the average. However, it just adds a prediction column with mean, but not variance.
Stefan
I have a basic regression problem with several numeric attributes. First, I perform feature subset selection, then model training, and then prediction on test data. By the way, it is awesome, that RapidMiner let's me do all those things without hassle. However, I encountered some situations where I would like to get more information regarding the stability of the methods I used.
I have two questions:
(1) Is it possible to get variance from "Loop and Average" regarding the averagable in addition to mean? This works for performance vectors, but not weight vectors.
I built a module for feature subset selection which returns the best subset. It is represented by an attribute weight vector, i.e. 0 = not selected, 1 = selected. This process has a random component and I repeat it with "Loop and Average" to get different random seeds, and average the selection vectors. The result should be a table of each attribute and their relative selection count with mean and variance. However, I only get mean. I tried a workaround with "Log". I can get the performances for each cross-validation fold, and also each cross-validation itself using "applycount" as primary and foreign keys. However, I cannot get any information from the "Optimize Selection" operator, e.g. "feature_names" always delivers "?". Please, have a look at the logs in the example.
(2) Is it possible to get variance from "Bagging" in combination with "Apply Model" regarding the label in addition to mean? This also does not seem to work with "X-Prediction".
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.3.005">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="5.3.005" expanded="true" name="Process">
<process expanded="true">
<operator activated="true" class="retrieve" compatibility="5.3.005" expanded="true" height="60" name="Retrieve" width="90" x="112" y="30">
<parameter key="repository_entry" value="//Samples/data/Polynomial"/>
</operator>
<operator activated="true" class="sample" compatibility="5.3.005" expanded="true" height="76" name="Sample" width="90" x="313" y="30">
<parameter key="sample_size" value="50"/>
<list key="sample_size_per_class"/>
<list key="sample_ratio_per_class"/>
<list key="sample_probability_per_class"/>
</operator>
<operator activated="true" class="loop_and_average" compatibility="5.3.005" expanded="true" height="94" name="Loop and Average" width="90" x="514" y="30">
<process expanded="true">
<operator activated="true" class="optimize_selection_brute_force" compatibility="5.3.005" expanded="true" height="94" name="Optimize Selection (Brute Force)" width="90" x="380" y="30">
<parameter key="max_number_of_attributes" value="3"/>
<process expanded="true">
<operator activated="true" class="x_validation" compatibility="5.3.005" expanded="true" height="112" name="X-Validation" width="90" x="380" y="30">
<parameter key="sampling_type" value="shuffled sampling"/>
<process expanded="true">
<operator activated="true" class="support_vector_machine" compatibility="5.3.005" expanded="true" height="112" name="SVM" width="90" x="179" y="30">
<parameter key="kernel_type" value="polynomial"/>
<parameter key="kernel_degree" value="3.0"/>
<parameter key="C" value="1.0"/>
</operator>
<connect from_port="training" to_op="SVM" to_port="training set"/>
<connect from_op="SVM" 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="5.3.005" expanded="true" height="76" name="Apply Model" width="90" x="112" y="30">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="performance_regression" compatibility="5.3.005" expanded="true" height="76" name="Performance" width="90" x="313" y="30">
<parameter key="main_criterion" value="correlation"/>
<parameter key="correlation" value="true"/>
</operator>
<operator activated="true" class="log" compatibility="5.3.005" expanded="true" height="76" name="Log Folds" width="90" x="514" y="30">
<list key="log">
<parameter key="FoldID" value="operator.Performance.value.applycount"/>
<parameter key="Performance" value="operator.Performance.value.performance"/>
<parameter key="ValidationID" value="operator.X-Validation.value.applycount"/>
</list>
</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_op="Log Folds" to_port="through 1"/>
<connect from_op="Log Folds" from_port="through 1" 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="log" compatibility="5.3.005" expanded="true" height="76" name="Log Validation" width="90" x="581" y="30">
<list key="log">
<parameter key="ValidationID" value="operator.X-Validation.value.applycount"/>
<parameter key="Performance" value="operator.X-Validation.value.performance"/>
<parameter key="Deviation" value="operator.X-Validation.value.deviation"/>
<parameter key="LoopID" value="operator.Optimize Selection (Brute Force).value.applycount"/>
</list>
</operator>
<connect from_port="example set" to_op="X-Validation" to_port="training"/>
<connect from_op="X-Validation" from_port="averagable 1" to_op="Log Validation" to_port="through 1"/>
<connect from_op="Log Validation" from_port="through 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>
<operator activated="true" class="log" compatibility="5.3.005" expanded="true" height="112" name="Log Loop" width="90" x="581" y="30">
<list key="log">
<parameter key="LoopID" value="operator.Optimize Selection (Brute Force).value.applycount"/>
<parameter key="Feature Names" value="operator.Optimize Selection (Brute Force).value.feature_names"/>
<parameter key="Performance" value="operator.Optimize Selection (Brute Force).value.performance"/>
<parameter key="Best" value="operator.Optimize Selection (Brute Force).value.best"/>
</list>
</operator>
<connect from_port="in 1" to_op="Optimize Selection (Brute Force)" to_port="example set in"/>
<connect from_op="Optimize Selection (Brute Force)" from_port="example set out" to_op="Log Loop" to_port="through 3"/>
<connect from_op="Optimize Selection (Brute Force)" from_port="weights" to_op="Log Loop" to_port="through 1"/>
<connect from_op="Optimize Selection (Brute Force)" from_port="performance" to_op="Log Loop" to_port="through 2"/>
<connect from_op="Log Loop" from_port="through 1" to_port="averagable 2"/>
<connect from_op="Log Loop" from_port="through 2" to_port="averagable 1"/>
<portSpacing port="source_in 1" spacing="0"/>
<portSpacing port="source_in 2" spacing="0"/>
<portSpacing port="sink_averagable 1" spacing="0"/>
<portSpacing port="sink_averagable 2" spacing="0"/>
<portSpacing port="sink_averagable 3" spacing="0"/>
</process>
</operator>
<connect from_op="Retrieve" from_port="output" to_op="Sample" to_port="example set input"/>
<connect from_op="Sample" from_port="example set output" to_op="Loop and Average" to_port="in 1"/>
<connect from_op="Loop and Average" from_port="averagable 1" to_port="result 1"/>
<connect from_op="Loop and Average" from_port="averagable 2" 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"/>
</process>
</operator>
</process>
I built a module for model training and prediction. Training is basically bagging applied to the induction algorithm. Then, I use "Apply Model" for prediction which should internally apply each bag model to the sample and compute the average. However, it just adds a prediction column with mean, but not variance.
Best regards,
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<process version="5.3.005">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="5.3.005" expanded="true" name="Process">
<process expanded="true">
<operator activated="true" class="retrieve" compatibility="5.3.005" expanded="true" height="60" name="Retrieve" width="90" x="112" y="30">
<parameter key="repository_entry" value="//Samples/data/Polynomial"/>
</operator>
<operator activated="true" class="split_data" compatibility="5.3.005" expanded="true" height="94" name="Split Data" width="90" x="313" y="30">
<enumeration key="partitions">
<parameter key="ratio" value="0.7"/>
<parameter key="ratio" value="0.3"/>
</enumeration>
</operator>
<operator activated="true" class="select_attributes" compatibility="5.3.005" expanded="true" height="76" name="Select Attributes" width="90" x="514" y="30">
<parameter key="attribute_filter_type" value="subset"/>
<parameter key="attributes" value="|a3|a2|a1|label"/>
</operator>
<operator activated="true" class="bagging" compatibility="5.3.005" expanded="true" height="76" name="Bagging" width="90" x="715" y="30">
<parameter key="iterations" value="100"/>
<process expanded="true">
<operator activated="true" class="support_vector_machine" compatibility="5.3.005" expanded="true" height="112" name="SVM" width="90" x="514" y="30">
<parameter key="kernel_type" value="polynomial"/>
<parameter key="kernel_degree" value="3.0"/>
<parameter key="C" value="1.0"/>
</operator>
<connect from_port="training set" to_op="SVM" to_port="training set"/>
<connect from_op="SVM" from_port="model" to_port="model"/>
<portSpacing port="source_training set" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
</process>
</operator>
<operator activated="true" class="apply_model" compatibility="5.3.005" expanded="true" height="76" name="Apply Model" width="90" x="715" y="255">
<list key="application_parameters"/>
</operator>
<operator activated="true" class="performance_regression" compatibility="5.3.005" expanded="true" height="76" name="Performance" width="90" x="916" y="255">
<parameter key="main_criterion" value="correlation"/>
<parameter key="correlation" value="true"/>
</operator>
<connect from_op="Retrieve" from_port="output" to_op="Split Data" to_port="example set"/>
<connect from_op="Split Data" from_port="partition 1" to_op="Select Attributes" to_port="example set input"/>
<connect from_op="Split Data" from_port="partition 2" to_op="Apply Model" to_port="unlabelled data"/>
<connect from_op="Select Attributes" from_port="example set output" to_op="Bagging" to_port="training set"/>
<connect from_op="Bagging" from_port="model" to_op="Apply Model" to_port="model"/>
<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="result 1"/>
<connect from_op="Performance" 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"/>
</process>
</operator>
</process>
Stefan
Tagged:
0
Answers
-
Hi Stefan,
the log values of Optimize Selection are available only from inside the operator, so you can log the performance etc. of each iteration. However, it is currently only possible to log the features of the previous iteration - I have filed a bug for that.
To get the feature names *after* its execution transform the weight vector into an example set with Weights to Data and the aggregate that data with the Aggregate operator and the concat aggregation function.
From Loop and Average seemingly you can't any variances. However, since your data is always 0 or 1, you can derive the variance from the mean.
Also for the bagging on regression problems it is not possible to get the variance, though this would be a good idea. I will create a feature request for that.
Best regards, and happy mining!
~Marius0 -
Thanks, that was very helpful!
Indeed, variance does not give any more information than mean for the attribute weight vector. But, now that I can log the exact feature subsets and performance values for each loop, I can check for outliers or look at individual cross-validation variances.
0