Naive Bayes Learner

The 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

Classification Column

The class value column.

Default probability

A probability of zero for a given attribute/class value pair requires special attention. Without adjustment, a probability of zero would exercise an absolute veto over a likelihood in which that probability appears as a factor. Therefore, the Bayes model incorporates a default probability parameter that specifies a default (usually very small) probability to use in lieu of zero probability for a given attribute/class value pair. The default probability is used if the attribute is:

• nominal and was not seen by the learner
• continuous and its probability is smaller than the default probability

Minimum standard deviation

Specify the minimum standard deviation to use for observations without enough (diverse) data. The value must be at least 1e-10.

Threshold standard deviation

Specify the threshold for standard deviation. The value must be positive. If this threshold is not met, the minimum standard deviation value is used.

Maximum number of unique nominal values per attribute

All nominal columns with more unique values than the defined number will be skipped during learning.

Ignore missing values

By default the node uses the missing value information to improve the prediction result. Since the PMML standard does not support this option and ignores missing values this option is disabled if the PMML compatibility option is selected and missing values are ignored.

Create PMML 4.2 compatible model

Select this option to create a model which is compliant with the PMML 4.2 standard. The PMML 4.2 standard ignores missing values and does not support bit vectors. Therefore bit vector columns and missing values are ignored during learning and prediction if this option is selected.

Even if this option is not selected the node creates a valid PMML model. However the model contains KNIME specific information to store missing value and bit vector information. This information is used in the KNIME Naive Bayes Predictor to improve the prediction result but ignored by any other PMML compatible predictor which might result in different prediction results.

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

• !Modern UI_ABC-XYX-RFM AutoML SuperstoreKNIME Hub
• 0_install_AI_extensionsKNIME Hub
• 01_AutoML_Component_via_Interactive_ViewsKNIME Hub
• 01_Combining_Classifiers_using_Prediction_FusionKNIME Hub
• 01_Explainable_Artificial_Intelligence_(XAI)_SimpleKNIME Hub
• Show all 367 workflows

Links

• Export PMML that outputs class probabilitiesby mlauber71 on 2019-01-22

  forum.knime.com/p/53278
• Help!- Churn for Bank Customers Analyzingby gonhaddock on 2022-05-18

  forum.knime.com/p/157353

Developers