alpha : float, optional (default=1.0)
Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
fit_prior : boolean
Whether to learn class prior probabilities or not. If false, a uniform prior will be used.
More details
alpha is to prevent 0 likelihood of some features. usually, alpha in[0,1]
N{yi} is the number of times feature(word) i appear in documents of class y
N{y} is total number of all features(words) in documents of class y
n is the number of features(number of columns )
alpha : float, optional (default=1.0) Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing). fit_prior : boolean Whether to learn class prior probabilities or not. If false, a uniform prior will be used.
More details
alpha is to prevent 0 likelihood of some features. usually, alpha in[0,1]
N{yi} is the number of times feature(word) i appear in documents of class y N{y} is total number of all features(words) in documents of class y n is the number of features(number of columns )