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 ReplacementNaive Bayes LearnerThe node creates a Bayesian model from the given training data. It calculates the number of rows per attribute value per class for nominal attributes and the Gaussian distribution for numerical attributes. The created model could be used in the naive Bayes predictor to predict the class membership of unclassified data. The node displays a warning message if any columns are ignored due to unsupported data types. For example Bit Vector columns are ignored when the PMML compatibility flag is enabled since they are not supported by the PMML standard.
Options
Popular Predecessors
File Reader20 %- ARFF Reader11 %
CSV Reader9 %Scorer4 %Naive Bayes Predictor4 %Naive Bayes Learner3 %- Missing Value3 %
- Color Manager2 %
- Normalizer2 %
- SMOTE2 %
Database Reader2 %- Auto-Binner1 %
Row Splitter (deprecated)1 %Joiner1 %XLS Reader1 %Row Filter (deprecated)1 %- k-Means1 %
- GroupBy< 1 %
String To Number< 1 %- Row Sampling< 1 %
- Parameter Optimization Loop Start< 1 %
Column Rename< 1 %Excel Reader (XLS)< 1 %- Numeric Binner< 1 %
- Missing Value Column Filter< 1 %
- Statistics< 1 %
- Decision Tree Predictor< 1 %
- String Replacer< 1 %
Backward Feature Elimination Start (1:1)< 1 %- Correlation Filter< 1 %
- Concatenate< 1 %
- Column Splitter< 1 %
- Boosting Learner Loop Start< 1 %
- Domain Calculator< 1 %
- Rule Engine< 1 %
- Bootstrap Sampling< 1 %
Number To String (PMML)< 1 %Linear Correlation< 1 %- Nominal Value Row Filter< 1 %
- X-Aggregator< 1 %
K Nearest Neighbor< 1 %Read Excel Sheet Names (XLS)< 1 %Table Reader< 1 %- Fuzzy c-Means< 1 %
- Equal Size Sampling< 1 %
- String Manipulation< 1 %
- Data Generator< 1 %
- Math Formula< 1 %
- Category to Number< 1 %
- Hierarchical Clustering< 1 %
- Column Resorter< 1 %
- Normalizer (PMML)< 1 %
- Value Counter< 1 %
- Crosstab< 1 %
- Document Vector< 1 %
- Column Filter (legacy)< 1 %
Backward Feature Elimination Start (2:2)< 1 %- Java Snippet< 1 %
- Chunk Loop Start< 1 %
Column Appender< 1 %- Normalizer (Apply)< 1 %
- Table Creator< 1 %
Counting Loop Start< 1 %PCA Apply< 1 %- SVM Predictor< 1 %
- HiLite Row Splitter< 1 %
- Denormalizer< 1 %
- Rule-based Row Filter< 1 %
Read XLS Sheet Names< 1 %- Edit Nominal Domain< 1 %
- Outlier Removal< 1 %
Database Connection Table Reader< 1 %- Column List Loop Start< 1 %
- Cluster Assigner< 1 %
- CAIM Binner< 1 %
- Rank< 1 %
- RowID< 1 %
- Shuffle< 1 %
Color Appender< 1 %Python Script (1⇒1)< 1 %PCA< 1 %- Create Bit Vector< 1 %
Cell Splitter< 1 %Number To String< 1 %- Column Filter< 1 %
- Nominal Value Row Splitter< 1 %
- Table Transposer< 1 %
- Category to Class< 1 %
- Number to Category (Apply)< 1 %
- Low Variance Filter< 1 %
- PNN Learner (DDA)< 1 %
- PNN Predictor< 1 %
- Reference Column Filter< 1 %
- Missing Value (Apply)< 1 %
- ROC Curve (legacy)< 1 %
Excel Reader (XLS)< 1 %- Java Snippet (simple)< 1 %
Document vector< 1 %- Scatter Plot (JavaScript)< 1 %
- JSON to Table< 1 %
- Feature Selection Loop Start (1:1)< 1 %
- Loop End (Column Append)< 1 %
- Fuzzy Rule Learner< 1 %
- MDS< 1 %
- MultiLayerPerceptron Predictor< 1 %
- Logistic Regression Predictor< 1 %
- Distance Matrix Pair Extractor< 1 %
- Reference Row Filter< 1 %
- Column Filter (PMML)< 1 %
- DL4J Feedforward Learner (Regression) (legacy)< 1 %
- Java Edit Variable (simple)< 1 %
Meta Info Extractor< 1 %- Column Selection (legacy)< 1 %
Python Source< 1 %- R Snippet< 1 %
Extract Date&Time Fields< 1 %- Twitter Search (legacy)< 1 %
- Discretise< 1 %
- Fingerprints Expander< 1 %
- Logistic Regression Learner< 1 %
- Regression Predictor< 1 %
- Random Forest Learner< 1 %
- Create Byte Vector< 1 %
- Column Auto Type Cast< 1 %
- Double to Integer< 1 %
- Numeric Binner (PMML)< 1 %
- Number Rounder< 1 %
- Target Shuffling< 1 %
- Extract Table Dimension< 1 %
PMML Predictor< 1 %Bag of Words Creator< 1 %- Bag Of Words Creator< 1 %
- String to Date&Time< 1 %
- Virtual Screening Metrics< 1 %
Active Learn Loop End< 1 %- Split Collection Column< 1 %
Table Row to Variable< 1 %Table Row To Variable Loop Start< 1 %Parameterized Database Query< 1 %- Java Edit Variable< 1 %
- Feature Selection Loop End< 1 %
Interval Loop Start< 1 %- X-Partitioner< 1 %
- DBSCAN< 1 %
Concatenate (Optional in)< 1 %- Partitioning< 1 %
- Pivot< 1 %
- Rule-based Row Splitter< 1 %
- IDF< 1 %
- NGram Creator< 1 %
- Strings to Document< 1 %
- Term to String< 1 %
- Scorer (JavaScript)< 1 %
- Molecular Properties< 1 %
- Benchmark Start< 1 %
- R Snippet< 1 %
Table Column to Variable< 1 %- Read Images< 1 %
Line Reader< 1 %List Files< 1 %Backward Feature Elimination Filter< 1 %- Feature Selection Filter< 1 %
PCA Compute< 1 %- Linear Regression Learner< 1 %
- Auto-Binner (Apply)< 1 %
- Binner (Dictionary)< 1 %
- Cell Splitter< 1 %
- Column Comparator< 1 %
- Column Aggregator< 1 %
- Column Name Extractor< 1 %
- Column Merger< 1 %
- Table Validator< 1 %
- Constant Value Column Filter< 1 %
- Filter Apply< 1 %
- Interactive HiLite Collector< 1 %
- Normalizer Apply (PMML)< 1 %
String To Number (PMML)< 1 %- Decision Tree to Ruleset< 1 %
- Standardized Cronbach Alpha< 1 %
- Data Explorer< 1 %
Linear Discriminant Analysis< 1 %- Single sample t-test< 1 %
CASE Switch Data (Start)< 1 %- Cache< 1 %
- Extract Table Spec< 1 %
- Box Plot (legacy)< 1 %
- SDF Reader< 1 %
- Parallel Chunk Start< 1 %
- TimeDelay Loop Start< 1 %
- Visual 2D Data Generator< 1 %
- Java Snippet Row Splitter< 1 %
- DF< 1 %
- String Matcher< 1 %
- Case Converter< 1 %
- POS Tagger< 1 %
- Weka Predictor (3.7)< 1 %
- Double Input (legacy)< 1 %
- Decision Tree View (JavaScript)< 1 %
- Table View (JavaScript)< 1 %
- JSON Path< 1 %
MongoDB Reader< 1 %Python Script (1⇒1)< 1 %- Date&Time to String< 1 %
- Moving Average< 1 %
- String to Date/Time (legacy)< 1 %
- Fingerprints< 1 %
- RDKit Descriptor Calculation< 1 %
- RDKit R-Group Decomposition (deprecated)< 1 %
Recursive Loop Start< 1 %- String Input (legacy)< 1 %
Views
- Naive Bayes Learner View
- The view displays the learned model with the number of rows per class attribute. The number of rows per attribute per class for nominal attributes and the Gaussian distribution per class for numerical attributes.
Workflows
01_AnalyticsKNIME Hub- 01_AnalyticsKNIME Hub
- 01_Model_Selection_SampledKNIME Hub
- 03_ZIP_Local_vs_remoteHTTPKNIME Hub
- 14_Reading local and remote compressed CSV filesKNIME Hub
- Show all 9 workflows
Links
- No links available
Developers
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