How to use NaiveBayes Classifier?
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# How to use NaiveBayes Classifier?

This recipe helps you use NaiveBayes Classifier

## Recipe Objective

Naive Bayes classifiers are a collection of classification algorithms based on Bayes' Theorem. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other.

So this recipe is a short example on how to use NaiveBayes Classifier. Let's get started.

## Step 1 - Import the library

``` from sklearn import datasets from sklearn import metrics from sklearn.model_selection import train_test_split from sklearn.datasets import load_iris from sklearn.naive_bayes import GaussianNB ```

Let's pause and look at these imports. We have exported train_test_split which helps in randomly breaking the datset in two parts. Here sklearn.dataset is used to import one classification based model dataset. Also, we have exported Guassian Naive Bays library to build our model.

## Step 2 - Setup the Data

``` X,y=load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25) ```

Here, we have used load_iris function to import our dataset in two list form (X and y) and therefore kept return_X_y to be True. Further with have broken down the dataset into 2 parts, train and test with ratio 3:4.

## Step 3 - Building the model

``` model = GaussianNB() ```

We have simply built a classification model with GaussianNB with default values.

## Step 4 - Fit the model and predict for test set

``` model.fit(X_train, y_train) y_pred= model.predict(X_test) ```

Here we have simply fit used fit function to fit our model on X_train and y_train. Now, we are predicting the values of X_test using our built model.

## Step 5 - Printing the accuracy

``` print(metrics.accuracy_score(y_test, y_pred)*100) ```

Here we have calculated accuracy score using matrics library

## Step 6 - Lets look at our dataset now

Once we run the above code snippet, we will see:

```97.36842105263158
```

Clearly, the model built for the given datset in highly efficient.

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