- Accuracy plot python
- Naive Bayes Classifier: Learning Naive Bayes with Python
- Naive Bayes for text classification in Python
- In Depth: Naive Bayes Classification
- How to Develop a Naive Bayes Classifier from Scratch in Python
Accuracy plot pythonIf you find this content useful, please consider supporting the work by buying the book! The previous four sections have given a general overview of the concepts of machine learning. In this section and the ones that follow, we will be taking a closer look at several specific algorithms for supervised and unsupervised learning, starting here with naive Bayes classification. Naive Bayes models are a group of extremely fast and simple classification algorithms that are often suitable for very high-dimensional datasets. Because they are so fast and have so few tunable parameters, they end up being very useful as a quick-and-dirty baseline for a classification problem. This section will focus on an intuitive explanation of how naive Bayes classifiers work, followed by a couple examples of them in action on some datasets. Naive Bayes classifiers are built on Bayesian classification methods. These rely on Bayes's theorem, which is an equation describing the relationship of conditional probabilities of statistical quantities. Bayes's theorem tells us how to express this in terms of quantities we can compute more directly:. Such a model is called a generative model because it specifies the hypothetical random process that generates the data. Specifying this generative model for each label is the main piece of the training of such a Bayesian classifier. The general version of such a training step is a very difficult task, but we can make it simpler through the use of some simplifying assumptions about the form of this model. This is where the "naive" in "naive Bayes" comes in: if we make very naive assumptions about the generative model for each label, we can find a rough approximation of the generative model for each class, and then proceed with the Bayesian classification. Different types of naive Bayes classifiers rest on different naive assumptions about the data, and we will examine a few of these in the following sections. Perhaps the easiest naive Bayes classifier to understand is Gaussian naive Bayes. In this classifier, the assumption is that data from each label is drawn from a simple Gaussian distribution. Imagine that you have the following data:. One extremely fast way to create a simple model is to assume that the data is described by a Gaussian distribution with no covariance between dimensions. This model can be fit by simply finding the mean and standard deviation of the points within each label, which is all you need to define such a distribution. The result of this naive Gaussian assumption is shown in the following figure:. The ellipses here represent the Gaussian generative model for each label, with larger probability toward the center of the ellipses. This procedure is implemented in Scikit-Learn's sklearn. GaussianNB estimator:. We see a slightly curved boundary in the classifications—in general, the boundary in Gaussian naive Bayes is quadratic. The columns give the posterior probabilities of the first and second label, respectively. If you are looking for estimates of uncertainty in your classification, Bayesian approaches like this can be a useful approach. Of course, the final classification will only be as good as the model assumptions that lead to it, which is why Gaussian naive Bayes often does not produce very good results. Still, in many cases—especially as the number of features becomes large—this assumption is not detrimental enough to prevent Gaussian naive Bayes from being a useful method. The Gaussian assumption just described is by no means the only simple assumption that could be used to specify the generative distribution for each label. Another useful example is multinomial naive Bayes, where the features are assumed to be generated from a simple multinomial distribution. The multinomial distribution describes the probability of observing counts among a number of categories, and thus multinomial naive Bayes is most appropriate for features that represent counts or count rates. The idea is precisely the same as before, except that instead of modeling the data distribution with the best-fit Gaussian, we model the data distribuiton with a best-fit multinomial distribution. One place where multinomial naive Bayes is often used is in text classification, where the features are related to word counts or frequencies within the documents to be classified. We discussed the extraction of such features from text in Feature Engineering ; here we will use the sparse word count features from the 20 Newsgroups corpus to show how we might classify these short documents into categories. For simplicity here, we will select just a few of these categories, and download the training and testing set:. In order to use this data for machine learning, we need to be able to convert the content of each string into a vector of numbers. For this we will use the TF-IDF vectorizer discussed in Feature Engineeringand create a pipeline that attaches it to a multinomial naive Bayes classifier:. With this pipeline, we can apply the model to the training data, and predict labels for the test data:. Now that we have predicted the labels for the test data, we can evaluate them to learn about the performance of the estimator. For example, here is the confusion matrix between the true and predicted labels for the test data:.
Naive Bayes Classifier: Learning Naive Bayes with Python
Naive Bayes for text classification in Python
Naive Bayes classification makes use of Bayes theorem to determine how probable it is that an item is a member of a category. When we follow these rules, some words tend to be correlated with other words. I chose sub-disciplines that are distinct, but that have a significant amount of overlap: Epistemology and Ethics. Both employ the language of justification and reasons. They also intersect frequently e. In the end, Naive Bayes performed surprisingly well in classifying these documents. What is Naive Bayes Classification? Bayes Theorem. Bayes theorem tells us that the probability of a hypothesis given some evidence is equal to the probability of the hypothesis multiplied by the probability of the evidence given the hypothesis, then divided by the probability of the evidence. Since classification tasks involve comparing two or more hypotheses, we can use the ratio form of Bayes theorem, which compares the numerators of the above formula for Bayes aficionados: the prior times the likelihood for each hypothesis:. Since there are many words in a document, the formula becomes:. A demonstration: Classifying philosophy papers by their abstracts. The documents I will attempt to classify are article abstracts from a database called PhilPapers. Philpapers is a comprehensive database of research in philosophy. Since this database is curated by legions of topic editors, we can be reasonably confident that the document classifications given on the site are correct. I selected two philosophy subdisciplines from the site for a binary Naive Bayes classifier: ethics or epistemology. From each subdiscipline, I selected a topic. The head and tail of my initial DataFrame looked like this:. To run a Naive Bayes classifier in Scikit Learn, the categories must be numeric, so I assigned the label 1 to all ethics abstracts and the label 0 to all epistemology abstracts that is, not ethics :. Split data into training and testing sets. Convert abstracts into word count vectors. A Naive Bayes classifier needs to be able to calculate how many times each word appears in each document and how many times it appears in each category. To make this possible, the data needs to look something like this:. Each row represents a document, and each column represents a word. CountVectorizer creates a vector of word counts for each abstract to form a matrix. Each index corresponds to a word and every word appearing in the abstracts is represented. For details, see the documentation. Fit the model and make predictions. Check the results. To understand these scores, it helps to see a breakdown:. The accuracy score tells us: out of all of the identifications we made, how many were correct? The precision score tells us: out of all of the ethics identifications we made, how many were correct? The recall score tells us: out of all of the true cases of ethics, how many did we identify correctly? To investigate the incorrect labels, we can put the actual labels and the predicted labels side-by-side in a DataFrame. Overall, my Naive Bayes classifier performed well on the test set. There were only three mismatched labels out of Recommended reading:. Sign in. Naive Bayes Document Classification in Python.
In Depth: Naive Bayes Classification
I am going to use Multinomial Naive Bayes and Python to perform text classification in this tutorial. I am going to use the 20 Newsgroups data set, visualize the data set, preprocess the text, perform a grid search, train a model and evaluate the performance. Naive Bayes is a group of algorithms that is used for classification in machine learning. Naive Bayes classifiers are based on Bayes theorem, a probability is calculated for each category and the category with the highest probability will be the predicted category. Gaussian Naive Bayes deals with continuous variables that are assumed to have a normal Gaussian distribution. Multinomial Naive Bayes deals with discrete variables that is a result from counting and Bernoulli Naive Bayes deals with boolean variables that is a result from determining an existence or not. Multinomial Naive Bayes takes word count into consideration while Bernoulli Naive Bayes only takes word occurrence into consideration when we are working with text classification. Bernoulli Naive Bayes may be prefered if we do not need the added complexity that is offered by Multinomial Naive Bayes. We are going to use the 20 Newsgroups data set download it in this tutorial. You shall download 20news-bydate. You will need to have the following libraries: pandas, joblib, numpy, matplotlib, nltk and scikit-learn. I have created a common module common. This function will process each article in the data set and remove headers, footers, quotes, punctations and digits. I am also using a stemmer to stem each word in each article, this process takes some time and you may want to comment this line to speed things up. You can use a lemmatizer instead of a stemmer if you want, you might need to download WordNetLemmatizer. The code to visualize the data set is included in the training module. We mainly want to see the balance of the training set, a balanced data set is important in classification algorithms. The data set is not perfectly balanced, the most frequent category rec. The probability of correctly predicting the most frequent category at random is 5. I am doing a grid search to find the best parameters to use for training. A grid search can take a long time to perform on large data sets and you can therefore slice the data set and perform the grid search on a smaller set. The ouput from this process is shown below and I am going to use these parameters when I train the model. Evaluation is made on the training set and with cross-validation. The cross-validation evaluation will give a hint on the generalization performance of the model. I had Testing and evaluation is performed in the evaluation module. I am loading files from the 20news-bydate-test folder, I preprocess the test data, I load models and I evaluate the performance. The output from the evaluation is shown below.