Binary Classification using GaussianNB, MultinomialNB, BernoulliNB classifiers
Binary classification is a type of supervised learning where the goal is to predict one of two possible outcomes, such as “positive” or “negative”. The Naive Bayes Classifier is a popular algorithm for binary classification, and it is implemented in the scikit-learn library (sklearn) in three different forms: GaussianNB, MultinomialNB and BernoulliNB.
GaussianNB is used when the data is continuous and follows a normal distribution. It is based on the assumption that the likelihood of a feature is a Gaussian distribution. This implementation is particularly useful when the features of the data are continuous, such as in problems related to image processing, speech recognition, and natural language processing.
MultinomialNB is used when the data is discrete, and the features represent the occurrence of events. It is based on the assumption that the features are generated by a multinomial distribution. This implementation is particularly useful when the features of the data are counts, such as in problems related to text classification, document classification, and sentiment analysis.
BernoulliNB is also used when the data is discrete, but the features represent binary events. It is based on the assumption that the features are generated by a Bernoulli distribution. This implementation is particularly useful when the features of the data are binary, such as in problems related to spam detection, fraud detection, and customer churn prediction.
The first step in using these classifiers is to prepare the dataset, this dataset should contain labeled examples of the two possible outcomes. The dataset should also be split into a training set and a test set. The training set is used to train the algorithm, and the test set is used to evaluate its performance.
After selecting the appropriate classifier and preparing the data, we can train the classifier on the training set using the
fit() function. The classifier will learn the patterns in the data and use them to make predictions about the outcomes of new examples. After the classifier is trained, we can evaluate its performance on the test set using the
predict() function. This function will take in the test set and return an array of predicted labels for each example in the test set. We can then compare the predicted labels with the actual labels to calculate the accuracy of the classifier.
Finally, we can use the trained classifier to make predictions on new, unseen examples. This can be done by calling the
predict() function on the trained classifier and passing in the new examples.
In summary, using Naive Bayes Classifier for binary classification in scikit-learn (sklearn) involves selecting the appropriate classifier from the GaussianNB, MultinomialNB and BernoulliNB based on the nature of data. Preparing a labeled dataset, splitting it into a training set and a test set. Training the classifier on the training set using the
fit() function and evaluating its performance on the test set using the
predict() function. Finally, using the trained classifier to make predictions on new unseen examples by calling the
predict() function on the trained classifier and passing in the new examples. Each of these implementations is suited for different types of data, and the choice of implementation will depend on the nature of the data.
In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in Python programming: Binary Classification using GaussianNB, MultinomialNB, BernoulliNB classifiers.
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