Naive Bayes Optimization
These are the most commonly adjusted parameters with different Naive Bayes Algorithms. Let’s take a deeper look at what they are used for and how to change their values:
Gaussian Naive Bayes Parameters:
Parameters for: Multinomial Naive Bayes, Complement Naive Bayes, Bernoulli Naive Bayes, Categorical Naive Bayes
priors: Concerning the prior class probabilities, when priors are provided (in an array) they won’t be adjusted based on the dataset.
var_smoothing: (default 1e-9)Concerning variance smoothing, float value provided will be used to calculate the largest variances of each feature and add it to the stability calculation variance
alpha: (default 1.0) Another smoothing parameter alpha can be used for Laplace Lidstone smoothing in various Naive Bayes Algorithms.
0: No smoothing will be applied
float: Smoothing will be applied at the amount of float assigned.
fit_prior: (default: True)
True: Prior probabilities for classes will be learned.
False: A uniform prior will be used for class prior probabilities.
class_prior: (default: None) Refers to class prior probabilities.
None: Priors will be adjusted based on the dataset.
Array: Priors will have pre-defined class probabilities and won’t be adjusted based on the data.
from sklearn.naive_bayes import GaussianNB GNB = GaussianNB(var_smoothing=2e-9)
from sklearn.naive_bayes import MultinomialNB MNB = MultinomialNB(alpha=0.6)
from sklearn.naive_bayes import BernoulliNB BNB = BernoulliNB(fit_prior = False)
from sklearn.naive_bayes import ComplementNB CNB = ComplementNB(norm = True)
More Naive Bayes Parameters for fine tuning
Further on, these parameters can be used for further optimization, to avoid overfitting and make adjustments based on impurity:
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