# GaussianNB

### *class* cuml.naive_bayes.GaussianNB(, priors=None, var_smoothing=1e-09, output_type=None, verbose=False)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/naive_bayes/naive_bayes.py#L223)

Gaussian Naive Bayes (GaussianNB)

Can perform online updates to model parameters via [`partial_fit()`](#cuml.naive_bayes.GaussianNB.partial_fit).
For details on algorithm used to update feature means and variance online,
see Stanford CS tech report STAN-CS-79-773 by Chan, Golub, and LeVeque:

> [http://i.stanford.edu/pub/cstr/reports/cs/tr/79/773/CS-TR-79-773.pdf](http://i.stanford.edu/pub/cstr/reports/cs/tr/79/773/CS-TR-79-773.pdf)
* **Parameters:**
  **priors**
  : Prior probabilities of the classes. If specified the priors are not
    adjusted according to the data.

  **var_smoothing**
  : Portion of the largest variance of all features that is added to
    variances for calculation stability.

  **output_type**
  : Return results and set estimator attributes to the indicated output
    type. If None, the output type set at the module level
    (`cuml.global_settings.output_type`) will be used. See
    [Output Data Type Configuration](../../cuml/index.html.md#output-data-type-configuration) for more info.

  **verbose**
  : Sets logging level. It must be one of `cuml.common.logger.level_*`.
    See [Verbosity Levels](../../cuml/index.html.md#verbosity-levels) for more info.
* **Attributes:**
  **class_prior_**

### Methods

| [`fit`](#cuml.naive_bayes.GaussianNB.fit)(X, y[, sample_weight])                  | Fit Gaussian Naive Bayes classifier according to X, y   |
|----------------------------------------------------------------------------------------------|---------------------------------------------------------|
| [`partial_fit`](#cuml.naive_bayes.GaussianNB.partial_fit)(X, y[, classes, sample_weight]) | Incremental fit on a batch of samples.                  |

### Examples

```pycon
>>> import cupy as cp
>>> from cuml.naive_bayes import GaussianNB
>>> X = cp.array(
...     [[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]],
...     dtype=cp.float32
... )
>>> y = cp.array([1, 1, 1, 2, 2, 2])
>>> clf = GaussianNB().fit(X, y)
>>> print(clf.predict(cp.array([[-0.8, -1]], cp.float32)))
[1]
```

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#### fit(X, y, sample_weight=None) → [GaussianNB](#cuml.naive_bayes.GaussianNB)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/naive_bayes/naive_bayes.py#L278)

Fit Gaussian Naive Bayes classifier according to X, y

* **Parameters:**
  **X**
  : Training vectors, where n_samples is the number of samples and
    n_features is the number of features.

  **y**
  : Target values.

  **sample_weight**
  : Weights applied to individual samples.

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#### get_params(deep=True)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/internals/base.py#L191)

Returns a dict of all params owned by this class. If the child class
has appropriately overridden the `_get_param_names` method and does not
need anything other than what is there in this method, then it doesn’t
have to override this method

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#### partial_fit(X, y, classes=None, sample_weight=None) → [GaussianNB](#cuml.naive_bayes.GaussianNB)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/naive_bayes/naive_bayes.py#L394)

Incremental fit on a batch of samples.

This method is expected to be called several times consecutively on
different chunks of a dataset so as to implement out-of-core or online
learning.

This is especially useful when the whole dataset is too big to fit in
memory at once.

This method has some performance overhead hence it is better to call
partial_fit on chunks of data that are as large as possible (as long
as fitting in the memory budget) to hide the overhead.

* **Parameters:**
  **X**
  : Training vectors, where n_samples is the number of samples and
    n_features is the number of features. A sparse matrix in COO
    format is preferred, other formats will go through a conversion
    to COO.

  **y**
  : Target values.

  **classes**
  : List of all the classes that can possibly appear in the y vector.
    Must be provided at the first call to partial_fit, can be omitted
    in subsequent calls.

  **sample_weight**
  : Weights applied to individual samples.
* **Returns:**
  **self**

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#### predict(X)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/naive_bayes/naive_bayes.py#L144)

Perform classification on an array of test vectors X.

* **Parameters:**
  **X**
  : Dense or sparse matrix with dtype float32 or float64.
    Acceptable dense formats: CUDA array interface compliant objects like
    CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas
    DataFrame/Series.
* **Returns:**
  **y_hat**
  : Predicted values
    <br/>
    For more information on how to configure cuML’s output type,
    refer to: [Output Data Type Configuration](../../cuml/index.html.md#output-data-type-configuration).

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#### predict_log_proba(X)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/naive_bayes/naive_bayes.py#L166)

Return log-probability estimates for the test vector X.

* **Parameters:**
  **X**
  : Dense or sparse matrix with dtype float32 or float64.
    Acceptable dense formats: CUDA array interface compliant objects like
    CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas
    DataFrame/Series.
* **Returns:**
  **C**
  : Returns the log-probability of the samples for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute `classes_`.
    <br/>
    For more information on how to configure cuML’s output type,
    refer to: [Output Data Type Configuration](../../cuml/index.html.md#output-data-type-configuration).

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#### predict_proba(X)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/naive_bayes/naive_bayes.py#L200)

Return probability estimates for the test vector X.

* **Parameters:**
  **X**
  : Dense or sparse matrix with dtype float32 or float64.
    Acceptable dense formats: CUDA array interface compliant objects like
    CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas
    DataFrame/Series.
* **Returns:**
  **C**
  : Returns the probability of the samples for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute `classes_`.
    <br/>
    For more information on how to configure cuML’s output type,
    refer to: [Output Data Type Configuration](../../cuml/index.html.md#output-data-type-configuration).

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#### score(X, y, sample_weight=None, \*\*kwargs)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/internals/mixins.py#L101)

Scoring function for classifier estimators based on mean accuracy.

* **Parameters:**
  **X**
  : Dense matrix with dtype float32 or float64.
    Acceptable formats: CUDA array interface compliant objects like
    CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas
    DataFrame/Series.

  **y**
  : Dense matrix with dtype float32 or float64.
    Acceptable formats: CUDA array interface compliant objects like
    CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas
    DataFrame/Series.

  **sample_weight**
  : The weights for each observation in X. If None, all observations
    are assigned equal weight.
    Acceptable formats: CUDA array interface compliant objects like
    CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas
    DataFrame/Series.
* **Returns:**
  **score**
  : Accuracy of self.predict(X) wrt. y (fraction where y == pred_y)

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#### set_params(\*\*params)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/internals/base.py#L200)

Accepts a dict of params and updates the corresponding ones owned by
this class. If the child class has appropriately overridden the
`_get_param_names` method and does not need anything other than what is,
there in this method, then it doesn’t have to override this method

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