# Naive Bayes Classifier (NBC)

#### Naive Bayes Classifier (NBC): A Step-by-Step Tutorial

Naive Bayes Classifier (NBC) is a simple yet powerful supervised machine learning algorithm used for classification tasks. In this tutorial, codeswithpankaj will guide you through the steps to perform Naive Bayes classification using Python.

**Table of Contents**

Introduction to Naive Bayes Classifier

Types of Naive Bayes Classifiers

Naive Bayes Intuition

Naive Bayes Assumptions

Naive Bayes Scikit-Learn Libraries

Dataset Description

Import Libraries

Import Dataset

Exploratory Data Analysis

Declare Feature Vector and Target Variable

Split Data into Separate Training and Test Set

Feature Scaling (if necessary)

Run Naive Bayes Classifier

Confusion Matrix

Classification Metrics

Stratified K-Fold Cross Validation

Hyperparameter Optimization Using GridSearchCV

Results and Conclusion

#### 1. Introduction to Naive Bayes Classifier

Naive Bayes Classifier is a probabilistic classifier based on Bayes' theorem with the "naive" assumption of conditional independence between every pair of features given the class label.

**Key Features**:

Simple and easy to implement.

Works well with small datasets.

Handles both binary and multi-class classification problems.

#### 2. Types of Naive Bayes Classifiers

**Gaussian Naive Bayes**: Assumes that the features follow a normal distribution.**Multinomial Naive Bayes**: Suitable for discrete data, often used for text classification.**Bernoulli Naive Bayes**: Suitable for binary/boolean features.

#### 3. Naive Bayes Intuition

Naive Bayes classifiers work by calculating the probability of each class based on the given features and selecting the class with the highest probability. It applies Bayes' theorem with strong (naive) independence assumptions.

#### 4. Naive Bayes Assumptions

The primary assumption of Naive Bayes is that all features are conditionally independent given the class label. While this assumption is rarely true in real-world data, Naive Bayes often performs well in practice.

#### 5. Naive Bayes Scikit-Learn Libraries

Scikit-learn provides easy-to-use implementations of Naive Bayes classifiers through `GaussianNB`

, `MultinomialNB`

, and `BernoulliNB`

classes.

#### 6. Dataset Description

We'll use the Iris dataset for this tutorial. The dataset contains three classes of iris plants, each with four features: sepal length, sepal width, petal length, and petal width.

#### 7. Import Libraries

First, we need to import the necessary libraries.

#### 8. Import Dataset

We'll load the Iris dataset directly from Scikit-learn.

#### 9. Exploratory Data Analysis

Let's take a look at the first few rows of the dataset to understand its structure.

#### 10. Declare Feature Vector and Target Variable

#### 11. Split Data into Separate Training and Test Set

#### 12. Feature Scaling (if necessary)

For Naive Bayes, feature scaling is generally not required, but it can be beneficial in some cases, especially when using GaussianNB.

#### 13. Run Naive Bayes Classifier

We'll start with the Gaussian Naive Bayes classifier.

#### 14. Confusion Matrix

#### 15. Classification Metrics

#### 16. Stratified K-Fold Cross Validation

#### 17. Hyperparameter Optimization Using GridSearchCV

For Gaussian Naive Bayes, there aren't many hyperparameters to tune. For Multinomial and Bernoulli Naive Bayes, we can tune the `alpha`

parameter.

#### 18. Results and Conclusion

In this tutorial by codeswithpankaj, we've covered the basics of Naive Bayes Classifier (NBC) and how to implement it using Python. We walked through setting up the environment, loading and exploring the data, preparing the data, building the model, evaluating the model, making predictions, and tuning the model. Naive Bayes is a simple yet powerful tool in data science for classification tasks.

For more tutorials and resources, visit codeswithpankaj.com.

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