Introduction AdaBoost classifier predictor AdaBoost classifier trainer AdaBoost regression predictor AdaBoost regression trainer Agglomerative clustering trainer Base supervised predictor Base supervised trainer Base unsupervised predictor Base unsupervised trainer BNB classifier predictor BNB classifier trainer Decision tree classifier predictor Decision tree classifier trainer Decision tree regressor predictor Decision tree regressor trainer Elastic-Net predictor ElasticNet trainer Extra-Trees classifier predictor Extra-Trees classifier trainer Extra-Trees regressor predictor Extra-Trees regressor trainer Gaussian mixture trainer Gaussian process classifier predictor Gaussian process classifier trainer Gaussian process regressor predictor Gaussian process regressor trainer GNB classifier trainer GNB predictor Gradient-Boosting classifier predictor Gradient-Boosting classifier trainer Gradient-Boosting regressor predictor Gradient-Boosting regressor trainer ICA trainer Kernel-Ridge predictor Kernel-Ridge trainer KMeans predictor KMeans trainer KNN classifier predictor KNN classifier trainer KNN regressor predictor KNN regressor trainer Lasso predictor Lasso trainer LDA predictor LDA trainer Linear regression predictor Linear regression trainer LLE trainer LMETrainer Logistic regression predictor Logistic regression trainer MNB classifier predictor MNB trainer PCA trainer PCoA trainer PLS regression predictor PLS regression trainer PLSDA predictor PLSDA trainer QDA predictor QDA trainer Random-Forest classifier predictor Random-Forest classifier trainer Random-Forest regression predictor Random-Forest regressor trainer Ridge classifier predictor Ridge classifier trainer Ridge regression predictor Ridge regression trainer SGD classifier predictor SGD classifier trainer SGD regressor predictor SGD regressor trainer SVC predictor SVC trainer SVC trainer SVR predictor
Technical Documentation
Resources
Tasks
Protocols
MNB trainer
Predict the class labels using a Multinomial Naive Bayes (MNB) classifier
Trainer of a naive Bayes classifier for a multinomial model. Fit a naive Bayes classifier according to a training table.
See https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.MultinomialNB.html for more details.
Input
Output
Configuration
training_design
Define the training design, i.e. the target Y to use for the model.
List-1target_name
The name of the 'columns' or 'row_tag keys' to use as targets or labels.
stringtarget_origin
The origin of the target. Notice: Targets comming from a 'row_tag' are always considered as categorical.
stringcolumn row_tag columntarget_type
The type of the target (categorical or numerical). Set 'auto' to infer the correct type. Notice: targets comming from row_tags are allways considered as categorical
stringauto categorical numerical autoalpha
float1