# Introduction In this lab, we will go through an example of using Naive Bayes classifiers from the scikit-learn library in Python. Naive Bayes classifiers are a set of supervised learning algorithms that are commonly used for classification tasks. These classifiers are based on applying Bayes' theorem with the assumption of conditional independence between every pair of features given the value of the class variable. In this example, we will use the Gaussian Naive Bayes classifier from scikit-learn to classify the iris dataset, which is a popular dataset for machine learning. The goal is to predict the species of an iris flower based on its petal and sepal dimensions. ## VM Tips After the VM startup is done, click the top left corner to switch to the **Notebook** tab to access Jupyter Notebook for practice. Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook. If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.
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