# bernoullinbfit

Applies Bernoulli Naïve Bayes model on the input data to compute parameters used for classification. Bernoulli Naïve Bayes is a probabilistic learning method which is used for classification. It assumes that each variable is independent of each other (Naïve assumption) and the input variables have binary values which is an indicator of event.

## Syntax

parameters = bernoullinbfit(X,y)

parameters = bernoullinbfit(X,y,options)

## Inputs

`X`- Training data.
`y`- Target values.
`options`- Type: struct

## Outputs

- parameters
- Contains all the values passed to bernoulliFit method as options. Additionally it has below key-value pairs.

## Example

Usage of bernoullinbfit with options

```
X = [1, 0, 1, 1;
0, 0, 1, 0;
1, 0, 1, 0];
y = [1, 2, 2]';
X_test = X(3, :);
options = struct;
options.binarize_threshold = 0;
options.alpha = 1;
parameters = bernoullinbfit(X, y, options);
```

```
> parameters
parameters = struct [
alpha: 1
binarize_threshold: 0
feature_count: [Matrix] 2 x 4
2 1 2 2
2 1 3 1
labels: [Matrix] 2 x 1
1
2
n_features: 4
n_samples: 3
params: [Matrix] 2 x 4
0.66667 0.33333 0.66667 0.66667
0.50000 0.25000 0.75000 0.25000
prior: [Matrix] 2 x 1
-1.09861
-0.40547
]
```

## Comments

Output 'parameters' should be passed as input to bernoullinbpredict function.