# Plot naive bayes python

## Naive Bayes Classifier: Learning Naive Bayes with Python

Naive Bayes model is easy to build and particularly useful for very large data sets. There are two parts to this algorithm:. The Naive Bayes classifier assumes that the presence of a feature in a class is unrelated to any other feature. It serves as a way to figure out conditional probability.

This relates the probability of the hypothesis before getting the evidence P Hto the probability of the hypothesis after getting the evidence, P H E.

## Naive Bayes Tutorial: Naive Bayes Classifier in Python

Go a little confused? So, according to Bayes Theorem, we can solve this problem. First, we need to find out the probability. So here we have our Data, which comprises of the Day, Outlook, Humidity, Wind Conditions and the final column being Play, which we have to predict.

Starting with our first industrial use, it is News Categorization, or we can use the term text classification to broaden the spectrum of this algorithm. News on the web is rapidly growing where each news site has its own different layout and categorization for grouping news. Each news article contents is tokenized categorized. In order to achieve better classification result, we remove the less significant words i.

We apply the naive Bayes classifier for classification of news contents based on news code.

Naive Bayes Classifier - Multinomial Bernoulli Gaussian Using Sklearn in Python - Tutorial 32

Naive Bayes classifiers are a popular statistical technique of e-mail filtering. They typically use a bag of words features to identify spam e-mail, an approach commonly used in text classification.

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Particular words have particular probabilities of occurring in spam email and in legitimate email. Nowadays modern hospitals are well equipped with monitoring and other data collection devices resulting in enormous data which are collected continuously through health examination and medical treatment. Weather is one of the most influential factors in our daily life, to an extent that it may affect the economy of a country that depends on occupation like agriculture.

Weather prediction has been a challenging problem in the meteorological department for years. Even after the technological and scientific advancement, the accuracy in prediction of weather has never been sufficient. A Bayesian approach based model for weather prediction is used, where posterior probabilities are used to calculate the likelihood of each class label for input data instance and the one with maximum likelihood is considered resulting output.

Here we have a dataset comprising of Observations of women aged 21 and older. The dataset describes instantaneous measurement taken from patients, like age, blood workup, the number of times pregnant. Each record has a class value that indicates whether the patient suffered an onset of diabetes within 5 years.

The values are 1 for Diabetic and 0 for Non-Diabetic. I ,ve broken the whole process down into the following steps:. The first thing we need to do is load our data file. The data is in CSV format without a header line or any quotes.

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We can open the file with the open function and read the data lines using the reader function in the CSV module. The summary of the training data collected involves the mean and the standard deviation for each attribute, by class value.The Naive Bayes theorem works on the basis of probability.

Some of the students are very afraid of probability. So, we make this tutorial very easy to understand. Finally, we will implement the Naive Bayes Algorithm to train a model and classify the data and calculate the accuracy in python language. Naive Bayes theorem ignores the unnecessary features of the given datasets to predict the result. Many cases, Naive Bayes theorem gives more accurate result than other algorithms.

The rules of the Naive Bayes Classifier Algorithm is given below:. In this article, we are focused on Gaussian Naive Bayes approach.

Gaussian Naive Bayes is widely used. This result is determined by the Naive Bayes algorithm. Here we use only Gaussian Naive Bayes Algorithm. The data set contains 50 samples of three species of Iris flower. Those are Iris virginica, Iris setosa, and Iris versicolor.

Four features were measured from each sample: the sepals and petals, length and the width of the in centimetres. Here we assign the features data of the flowers to the X variable. And the flower types target to the y variable. Here we create a gaussian naive bayes classifier as nv.

And calculate the accuracy score. We got the accuracy score as 1. The whole code is available in this file: Naive bayes classifier — Iris Flower Classification. To find a random example we need to assume any random X data which is not present in the input data table, by using the Naive Bayes theory we can determine the most expected target Y with help of input data table.

Here sample means Random X, which is not present in the given data. Then we can determine the target Y by using the Naive Bayes theory. ### How to Develop a Naive Bayes Classifier from Scratch in Python

Okay, thanks.It uses a meta-learning algorithm to learn how to best combine the predictions from two or more base machine learning algorithms. The benefit of stacking is that it can harness the capabilities of a range of well-performing models on a classification or regression task and make predictions that have better performance than any single model in the ensemble.

It involves combining the predictions from multiple machine learning models on the same dataset, like bagging and boosting. The approach to this question is to use another machine learning model that learns when to use or trust each model in the ensemble.

The architecture of a stacking model involves two or more base models, often referred to as level-0 models, and a meta-model that combines the predictions of the base models, referred to as a level-1 model. The meta-model is trained on the predictions made by base models on out-of-sample data.

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That is, data not used to train the base models is fed to the base models, predictions are made, and these predictions, along with the expected outputs, provide the input and output pairs of the training dataset used to fit the meta-model. The outputs from the base models used as input to the meta-model may be real value in the case of regression, and probability values, probability like values, or class labels in the case of classification.

The most common approach to preparing the training dataset for the meta-model is via k-fold cross-validation of the base models, where the out-of-fold predictions are used as the basis for the training dataset for the meta-model. The training data for the meta-model may also include the inputs to the base models, e. This can provide an additional context to the meta-model as to how to best combine the predictions from the meta-model.

Once the training dataset is prepared for the meta-model, the meta-model can be trained in isolation on this dataset, and the base-models can be trained on the entire original training dataset. Stacking is appropriate when multiple different machine learning models have skill on a dataset, but have skill in different ways. Another way to say this is that the predictions made by the models or the errors in predictions made by the models are uncorrelated or have a low correlation. Base-models are often complex and diverse.

As such, it is often a good idea to use a range of models that make very different assumptions about how to solve the predictive modeling task, such as linear models, decision trees, support vector machines, neural networks, and more. Other ensemble algorithms may also be used as base-models, such as random forests. The meta-model is often simple, providing a smooth interpretation of the predictions made by the base models. As such, linear models are often used as the meta-model, such as linear regression for regression tasks predicting a numeric value and logistic regression for classification tasks predicting a class label.

Although this is common, it is not required. The super learner may be considered a specialized type of stacking. Stacking is designed to improve modeling performance, although is not guaranteed to result in an improvement in all cases.

Achieving an improvement in performance depends on the complexity of the problem and whether it is sufficiently well represented by the training data and complex enough that there is more to learn by combining predictions. It is also dependent upon the choice of base models and whether they are sufficiently skillful and sufficiently uncorrelated in their predictions or errors. If a base-model performs as well as or better than the stacking ensemble, the base model should be used instead, given its lower complexity e.

The scikit-learn Python machine learning library provides an implementation of stacking for machine learning. First, confirm that you are using a modern version of the library by running the following script:.Last Updated on January 10, Classification is a predictive modeling problem that involves assigning a label to a given input data sample. The problem of classification predictive modeling can be framed as calculating the conditional probability of a class label given a data sample.

Bayes Theorem provides a principled way for calculating this conditional probability, although in practice requires an enormous number of samples very large-sized dataset and is computationally expensive. Instead, the calculation of Bayes Theorem can be simplified by making some assumptions, such as each input variable is independent of all other input variables.

Although a dramatic and unrealistic assumption, this has the effect of making the calculations of the conditional probability tractable and results in an effective classification model referred to as Naive Bayes.

In this tutorial, you will discover the Naive Bayes algorithm for classification predictive modeling. Discover bayes opimization, naive bayes, maximum likelihood, distributions, cross entropy, and much more in my new bookwith 28 step-by-step tutorials and full Python source code. In machine learning, we are often interested in a predictive modeling problem where we want to predict a class label for a given observation.

For example, classifying the species of plant based on measurements of the flower. Problems of this type are referred to as classification predictive modeling problems, as opposed to regression problems that involve predicting a numerical value. The observation or input to the model is referred to as X and the class label or output of the model is referred to as y.

Together, X and y represent observations collected from the domain, i. One approach to solving this problem is to develop a probabilistic model.

From a probabilistic perspective, we are interested in estimating the conditional probability of the class label, given the observation. For example, a classification problem may have k class labels y1, y2, …, yk and n input variables, X1, X2, …, Xn. We can calculate the conditional probability for a class label with a given instance or set of input values for each column x1, x2, …, xn as follows:.

The conditional probability can then be calculated for each class label in the problem and the label with the highest probability can be returned as the most likely classification. The conditional probability can be calculated using the joint probability, although it would be intractable. Bayes Theorem provides a principled way for calculating the conditional probability. Where the probability that we are interested in calculating P A B is called the posterior probability and the marginal probability of the event P A is called the prior.

We can frame classification as a conditional classification problem with Bayes Theorem as follows:. The prior P yi is easy to estimate from a dataset, but the conditional probability of the observation based on the class P x1, x2, …, xn yi is not feasible unless the number of examples is extraordinarily large, e.

As such, the direct application of Bayes Theorem also becomes intractable, especially as the number of variables or features n increases. The solution to using Bayes Theorem for a conditional probability classification model is to simplify the calculation.

The Bayes Theorem assumes that each input variable is dependent upon all other variables. This is a cause of complexity in the calculation. We can remove this assumption and consider each input variable as being independent from each other. This changes the model from a dependent conditional probability model to an independent conditional probability model and dramatically simplifies the calculation.

First, the denominator is removed from the calculation P x1, x2, …, xn as it is a constant used in calculating the conditional probability of each class for a given instance and has the effect of normalizing the result. Next, the conditional probability of all variables given the class label is changed into separate conditional probabilities of each variable value given the class label.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.

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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.

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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.

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Each index corresponds to a word and every word appearing in the abstracts is represented.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.

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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.