Create Naive Bayes classifier object by fitting training
data
Syntax
nb = NaiveBayes.fit(training, class)
nb = NaiveBayes.fit(..., 'param1',val1, 'param2',val2,
...)
Description
nb = NaiveBayes.fit(training, class) builds
a NaiveBayes classifier object nb. training is
an N-by-D numeric matrix of
training data. Rows of training correspond to observations;
columns correspond to features. class is a classing
variable for training (see Grouped Data) taking K distinct levels.
Each element of class defines which class the corresponding
row of training belongs to. training and class must
have the same number of rows.
nb = NaiveBayes.fit(..., 'param1',val1, 'param2',val2,
...) specifies one or more of the following name/value
pairs:
'Distribution' – a string
or a 1-by-D cell vector of strings, specifying
which distributions fit uses to model the data.
If the value is a string, fit models all the features
using one type of distribution. fit can also model
different features using different types of distributions. If the
value is a cell vector, its jth element specifies
the distribution fit uses for the jth
feature. The available types of distributions are:
| 'normal' (default) | Normal (Gaussian) distribution. |
| 'kernel' | Kernel smoothing density estimate. |
| 'mvmn' | Multivariate multinomial distribution for discrete data. fit assumes
each individual feature follows a multinomial model within a class.
The parameters for a feature include the probabilities of all possible
values that the corresponding feature can take. |
| 'mn' | Multinomial distribution for classifying the count-based data
such as the bag-of-tokens model. In the bag-of-tokens model, the value
of the jth feature is the number of occurrences
of the jth token in this observation, so it must
be a non-negative integer. When 'mn' is used, fit considers
each observation as multiple trials of a multinomial distribution,
and considers each occurrence of a token as one trial. The number
of categories (bins) in this multinomial model is the number of distinct
tokens, i.e., the number of columns of training. |
'Prior' – The prior probabilities
for the classes, specified as one of the following:
| 'empirical' (default) | fit estimates the prior probabilities from
the relative frequencies of the classes in training. |
| 'uniform' | The prior probabilities are equal for all classes. |
| vector | A numeric vector of length K specifying
the prior probabilities in the class order of class. |
| structure | A structure S containing class levels and
their prior probabilities. S must have two fields:
S.prob: A numeric vector of prior
probabilities. S.class: A vector of the same type as class,
containing unique class levels indicating the class for the corresponding
element of prob. S.class must contain all the K levels
in class. It can also contain classes that do not
appear in class. This can be useful if training is
a subset of a larger training set. fit ignores
any classes that appear in S.class but not in class.
|
If the prior probabilities don't sum to one, fit will
normalize them.
'KSWidth' – The bandwidth
of the kernel smoothing window. The default is to select a default
bandwidth automatically for each combination of feature and class,
using a value that is optimal for a Gaussian distribution. You can
specify the value as one of the following:
| scalar | Width for all features in all classes. |
| row vector | 1-by-D vector where the jth
element is the bandwidth for the jth feature in
all classes. |
| column vector | K-by-1 vector where the ith
element specifies the bandwidth for all features in the ith
class. K represents the number of class levels. |
| matrix | K-by-D matrix M where M(i,j) specifies
the bandwidth for the jth feature in the ith
class. |
| structure | A structure S containing class levels and
their bandwidths. S must have two fields:S.width – A numeric array
of bandwidths specified as a row vector, or a matrix with D columns. S.class – A vector of the
same type as class, containing unique class levels
indicating the class for the corresponding row of width.
|
'KSSupport' – The regions
where the density can be applied. It can be a string, a two-element
vector as shown below, or a 1-by-D cell array of
these values:
| 'unbounded' (default) | The density can extend over the whole real line. |
| 'positive' | The density is restricted to positive values. |
| [L,U] | A two-element vector specifying the finite lower bound L and
upper bound U for the support of the density. |
'KSType' – The type of
kernel smoother to use. It can be a string or a 1-by-D cell
array of strings. Each string can be 'normal' (default), 'box', 'triangle',
or 'epanechnikov'.
See Also
Naive Bayes Classification, Grouped Data
 | gmdistribution.fit | | fitdist |  |
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