This Node Is Deprecated — This version of the node has been replaced with a new and improved version. The old version is kept for backwards-compatibility, but for all new workflows we suggest to use the version linked below.
Go to Suggested ReplacementLogistic Regression LearnerPerforms a multinomial logistic regression. Select in the dialog a
target column (combo box on top), i.e. the response. The two
lists in the center of the dialog allow you to include only certain
columns which represent the (independent) variables.
Make sure the columns you want to have included being in the right
"include" list.
See article in wikipedia about
logistic regression
for an overview about the topic.
This particular implementation uses an iterative optimization procedure
termed Fisher's scoring in order to compute the model.
If the optional PMML inport is connected and contains
preprocessing operations in the TransformationDictionary those are
added to the learned model.
Potential Errors and Error Handling
The computation of the model is an iterative optimization process that requires some properties of the data set. This requires a reasonable distribution of the target values and non-constant, uncorrelated columns. While some of these properties are checked during the node execution you may still run into errors during the computation. The list below gives some ideas what might go wrong and how to avoid such situations.- Insufficient Information This is the case when the data does not provide enough information about one or more target categories. Try to get more data or remove rows for target categories that may cause the error. If you are interested in a model for one target category make sure to group the target column before. For instance, if your data contains as target categories the values "A", "B", ..., "Z" but you are only interested in getting a model for class "A" you can use a rule engine node to convert your target into "A" and "not A".
- Violation of Independence Logistic Regression is based on the assumption of statistical independence. A common preprocessing step is to us a correlation filter to remove highly correlated learning columns. Use a "Linear Correlation" along with a "Correlation Filter" node to remove redundant columns, whereby often it's sufficient to compute the correlation model on a subset of the data only.
- Separation Please see this article about separation for more information.
Options
Popular Successors
Regression Predictor69 %PMML Writer4 %Scorer3 %- ROC Curve (legacy)2 %
Logistic Regression Learner2 %- Statistics1 %
- Scatter Plot (legacy)< 1 %
- PMML to Cell< 1 %
- Decision Tree Predictor< 1 %
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CSV Writer< 1 %- Interactive Table (legacy)< 1 %
XLS Writer< 1 %- Rule-based Row Filter< 1 %
PMML Predictor< 1 %- Lift Chart (legacy)< 1 %
- Missing Value< 1 %
Backward Feature Elimination End< 1 %Naive Bayes Predictor< 1 %- Cluster Assigner< 1 %
- Partitioning< 1 %
- 2D/3D Scatterplot< 1 %
- X-Aggregator< 1 %
Linear Correlation< 1 %- Column Filter< 1 %
Model Writer< 1 %- Correlation Filter< 1 %
- Edit Nominal Domain< 1 %
- Decision Tree Learner< 1 %
- MultiLayerPerceptron Predictor< 1 %
- Hierarchical Clustering< 1 %
- Decision Tree to Image< 1 %
- Denormalizer< 1 %
- Rule Engine< 1 %
- Model to Cell< 1 %
Excel Writer (XLS)< 1 %Loop End< 1 %- Polynomial Regression Learner< 1 %
- Simple Regression Tree Predictor< 1 %
- PMML Model Appender< 1 %
- Table Validator< 1 %
- Normalizer (PMML)< 1 %
- Ruleset Predictor< 1 %
- Crosstab< 1 %
- Enrichment Plotter (legacy)< 1 %
- Histogram (legacy)< 1 %
- PMML to SQL< 1 %
- Model Loop End< 1 %
- Extract Variables (Data)< 1 %
Table Writer< 1 %K Nearest Neighbor< 1 %- Linear Regression Learner< 1 %
Tree Ensemble Learner< 1 %- Random Forest Predictor (Regression)< 1 %
- Concatenate< 1 %
- Missing Value Column Filter< 1 %
- Missing Value (Apply)< 1 %
- String Manipulation< 1 %
Odds and Risk Ratios< 1 %- Independent groups t-test< 1 %
- Line Plot (legacy)< 1 %
- JPMML Regression Predictor< 1 %
- Decision Tree View (JavaScript)< 1 %
- Table View (JavaScript)< 1 %
Binary Scorer< 1 %- R Plot< 1 %
- Read Images< 1 %
PMML Reader< 1 %- Java Edit Variable< 1 %
- Java Snippet< 1 %
Backward Feature Elimination Filter< 1 %Backward Feature Elimination Start (1:1)< 1 %- Loop End (Column Append)< 1 %
- X-Partitioner< 1 %
- Hierarchical Clustering (DistMatrix)< 1 %
- k-Means< 1 %
- RProp MLP Learner< 1 %
PCA Compute< 1 %Numeric Scorer< 1 %Tree Ensemble Predictor (Regression)< 1 %Simple Regression Tree Learner< 1 %- Tree Ensemble Learner (Regression)< 1 %
Tree Ensemble Predictor< 1 %Random Forest Predictor< 1 %Column Appender< 1 %- Domain Calculator< 1 %
- Edit Nominal Domain (Dictionary)< 1 %
Row Filter (deprecated)< 1 %- GroupBy< 1 %
Number To String (PMML)< 1 %- Sorter< 1 %
- Value Counter< 1 %
Rule Engine< 1 %Odds and Risk Ratios< 1 %Linear Discriminant Analysis< 1 %- Single sample t-test< 1 %
- Scatter Matrix (legacy)< 1 %
- Color Manager< 1 %
- Boosting Learner Loop End< 1 %
- Boosting Learner Loop Start< 1 %
Table to HTML< 1 %Table to PDF< 1 %XLS Sheet Appender< 1 %- Weka Predictor (3.7)< 1 %
- JPMML Classifier< 1 %
- Neighbor Filter< 1 %
- Rowset Filter< 1 %
- Add Table to R< 1 %
- R Learner< 1 %
- Coordinate to Latitude/Longitude< 1 %
Views
- Logistic Regression Result View
- Displays the estimated coefficients and error statistics. Note, that the estimated coefficients are not reliable when the standard error is high.
Workflows
01_Model_Selection_SampledKNIME Hub- 09_Teradata_Aster_meets_KNIME_TableKNIME Hub
- ClassificationKNIME Hub
- ExplorationKNIME Hub
- ExplorationKNIME Hub
- Show all 6 workflows
Links
- No links available
Developers
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