Naive Bayes
- class sklego.naive_bayes.BayesianGaussianMixtureNB(n_components=1, covariance_type='full', tol=0.001, reg_covar=1e-06, max_iter=100, n_init=1, init_params='kmeans', weight_concentration_prior_type='dirichlet_process', weight_concentration_prior=None, mean_precision_prior=None, mean_prior=None, degrees_of_freedom_prior=None, covariance_prior=None, random_state=None, warm_start=False, verbose=0, verbose_interval=10)[source]
Bases:
sklearn.base.BaseEstimator,sklearn.base.ClassifierMixinThe BayesianGaussianMixtureNB trains a Naive Bayes Classifier that uses a bayesian mixture of gaussians instead of merely training a single one.
You can pass any keyword parameter that scikit-learn’s Bayesian Gaussian Mixture Model uses and it will be passed along.
- fit(X: numpy.array, y: numpy.array) sklego.naive_bayes.BayesianGaussianMixtureNB[source]
Fit the model using X, y as training data.
- Parameters
X – array-like, shape=(n_columns, n_samples, ) training data.
y – array-like, shape=(n_samples, ) training data.
- Returns
Returns an instance of self.
- class sklego.naive_bayes.GaussianMixtureNB(n_components=1, covariance_type='full', tol=0.001, reg_covar=1e-06, max_iter=100, n_init=1, init_params='kmeans', weights_init=None, means_init=None, precisions_init=None, random_state=None, warm_start=False)[source]
Bases:
sklearn.base.BaseEstimator,sklearn.base.ClassifierMixinThe GaussianMixtureNB trains a Naive Bayes Classifier that uses a mixture of gaussians instead of merely training a single one.
You can pass any keyword parameter that scikit-learn’s Gaussian Mixture Model uses and it will be passed along.
- fit(X: numpy.array, y: numpy.array) sklego.naive_bayes.GaussianMixtureNB[source]
Fit the model using X, y as training data.
- Parameters
X – array-like, shape=(n_columns, n_samples, ) training data.
y – array-like, shape=(n_samples, ) training data.
- Returns
Returns an instance of self.