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Exploring the Naive Bayes Classifier Algorithm with Iris Dataset in Python
In the field of machine learning, Naive Bayes classifier is a popular algorithm used for classification tasks such as text classification, spam filtering, and sentiment analysis. It is a probabilistic algorithm that uses Bayes’ theorem to predict the likelihood of a sample belonging to a certain class.
In this article, we will explore the Naive Bayes classifier algorithm and its implementation using Python’s scikit-learn library. Specifically, we will use the famous Iris dataset to train our model and make predictions. By the end of this tutorial, readers will have a better understanding of how Naive Bayes classifier works and how to apply it to real-world problems.
Step 1: Load the Data
First, we need to load the Iris dataset. The Iris dataset contains 150 samples of Iris flowers, each with 4 features: sepal length, sepal width, petal length, and petal width. There are 3 classes: Iris Setosa, Iris Versicolor, and Iris Virginica. We’ll use the load_iris() function from scikit-learn to load…
