LearningSVM

Support-vector machines. Trainers in the scikit-learn SVM family, behind Learning's typed contract. Each requires a clean training set (``independent_target`` and no missing values) and stamps the model with the dataset's feature/class count. ``open LearningSVM`` brings the core types in scope too.
Imports
open LearningCore;
Table of Contents

Functions

svc

def svc (1 ds : Dataset | independent_target ds = True && has_missing ds = False) : {m : SklearnClassifier | clf_features m = ds_features ds && clf_classes m = ds_classes ds}

linear_svc

No predict_proba; one-hot fallback.
def linear_svc (1 ds : Dataset | independent_target ds = True && has_missing ds = False) : {m : SklearnClassifier | clf_features m = ds_features ds && clf_classes m = ds_classes ds}

nu_svc

def nu_svc (1 ds : Dataset | independent_target ds = True && has_missing ds = False) : {m : SklearnClassifier | clf_features m = ds_features ds && clf_classes m = ds_classes ds}

svr

def svr (1 ds : Dataset | independent_target ds = True && has_missing ds = False) : {m : SklearnRegressor | reg_features m = ds_features ds}

linear_svr

def linear_svr (1 ds : Dataset | independent_target ds = True && has_missing ds = False) : {m : SklearnRegressor | reg_features m = ds_features ds}

nu_svr

def nu_svr (1 ds : Dataset | independent_target ds = True && has_missing ds = False) : {m : SklearnRegressor | reg_features m = ds_features ds}