Naive Bayes Basics

Naive Bayes Basics | CodeSignal Learn

Lesson Introduction

Hey there! Today we are going to explore an exciting topic in machine learning called Naive Bayes . By the end of this lesson, you'll understand what Naive Bayes is and how to implement it using Python's Scikit-Learn library. Let’s dive in!

P(C|X) = \frac{P(X|C) \cdot P(C)}{P(X)}

Naive Bayes updates its likelihoods and priors using the training data. When the model encounters new data, it breaks the data into its constituent features and applies Bayes' Theorem to calculate the class probabilities. The class with the highest probability is the predicted class. We will focus on GaussianNB, commonly used when features are continuous and assumed to follow a normal (Gaussian) distribution.

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Before training our Naive Bayes classifier, we need data. Consider it like needing mystery stories before solving them! We'll use the Iris dataset, which includes features of iris flowers to classify them into species. Let’s quickly remind ourselves how to load the dataset using Scikit-Learn : Python from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split # Load the Iris dataset X, y = load_iris(return_X_y=True) # Split the dataset into training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42) from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split # Load the Iris dataset X, y = load_iris(return_X_y=True) # Split the dataset into training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42)

Now that we have our data split, it’s time to train our Naive Bayes classifier using GaussianNB : Python from sklearn.naive_bayes import GaussianNB # Initialize the Naive Bayes classifier nb_clf = GaussianNB() # Train the classifier with training data nb_clf.fit(X_train, y_train) from sklearn.naive_bayes import GaussianNB # Initialize the Naive Bayes classifier nb_clf = GaussianNB() # Train the classifier with training data nb_clf.fit(X_train, y_train) Here, fit trains the model using the training data, much like a student learning from textbooks.

After training the model, let’s make predictions on the test data and calculate the accuracy: Pythonfrom sklearn.metrics import accuracy_score # Make predictions on the testing set y_pred = nb_clf.predict(X_test) # Calculate accuracy accuracy = accuracy_score(y_test, y_pred) print(f"Bayes model accuracy: {accuracy * 100:.2f}%") # Bayes model accuracy: 96.67%from sklearn.metrics import accuracy_score # Make predictions on the testing set y_pred = nb_clf.predict(X_test) # Calculate accuracy accuracy = accuracy_score(y_test, y_pred) print(f"Bayes model accuracy: {accuracy * 100:.2f}%") # Bayes model accuracy: 96.67% Here, y_pred contains the predicted class labels for the test set, and accuracy_score compares these predictions to the true labels (y_test) to calculate the model's accuracy.