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42 lines
2.2 KiB
Python
42 lines
2.2 KiB
Python
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Sure! The following is a python script that demonstrates the Naïve Bayes algorithm using the famous Iris dataset:
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```python
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import numpy as np
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from sklearn.datasets import load_iris
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from sklearn.model_selection import train_test_split
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from sklearn.naive_bayes import GaussianNB
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from sklearn.metrics import accuracy_score
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# Load the Iris dataset
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iris = load_iris()
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# Split the dataset into training and testing sets
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X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)
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# Create an instance of the Naïve Bayes classifier
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classifier = GaussianNB()
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# Train the classifier using the training data
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classifier.fit(X_train, y_train)
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# Make predictions on the testing data
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y_pred = classifier.predict(X_test)
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# Calculate accuracy of the model
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accuracy = accuracy_score(y_test, y_pred)
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print("Accuracy:", accuracy)
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```
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In this script, we start by importing the necessary libraries: `numpy` for numerical operations, `sklearn.datasets` to load the Iris dataset, `sklearn.model_selection` to split the data into training and testing sets, `sklearn.naive_bayes` for the Naïve Bayes classifier, and `sklearn.metrics` for calculating accuracy.
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Next, we load the Iris dataset using `load_iris()` function. Then we split the data into training and testing sets using `train_test_split()` function, where `test_size=0.2` indicates that 20% of the data will be used for testing.
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We create an instance of the Naïve Bayes classifier using `GaussianNB()`. This classifier assumes that features follow a Gaussian distribution. If your data doesn't meet this assumption, you can explore other variants like multinomial or Bernoulli Naïve Bayes.
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We train the classifier using the training data by calling the `fit()` method and passing in the features (X_train) and corresponding labels (y_train).
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Then, we make predictions on the testing data using the `predict()` method and passing in the features of the test set (X_test).
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Finally, we calculate the accuracy of the classifier by comparing the predicted labels with the true labels from the testing set using the `accuracy_score()` function.
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Hope this helps to demonstrate the Naïve Bayes algorithm in python!
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