- In Depth: Naive Bayes Classification
- Naive Bayes Algorithm in Python
- Naive Bayes Classifier: Learning Naive Bayes with Python
- Naive Bayes for text classification in Python
- How to Develop a Naive Bayes Classifier from Scratch in Python
In Depth: Naive Bayes ClassificationIn this tutorial you are going to learn about the Naive Bayes algorithm including how it works and how to implement it from scratch in Python without libraries. We can use probability to make predictions in machine learning. Perhaps the most widely used example is called the Naive Bayes algorithm. Not only is it straightforward to understand, but it also achieves surprisingly good results on a wide range of problems. Discover how to code ML algorithms from scratch including kNN, decision trees, neural nets, ensembles and much more in my new bookwith full Python code and no fancy libraries. This section provides a brief overview of the Naive Bayes algorithm and the Iris flowers dataset that we will use in this tutorial. Naive Bayes is a classification algorithm for binary two-class and multiclass classification problems. It is called Naive Bayes or idiot Bayes because the calculations of the probabilities for each class are simplified to make their calculations tractable. Rather than attempting to calculate the probabilities of each attribute value, they are assumed to be conditionally independent given the class value. This is a very strong assumption that is most unlikely in real data, i. Nevertheless, the approach performs surprisingly well on data where this assumption does not hold. The Iris Flower Dataset involves predicting the flower species given measurements of iris flowers. It is a multiclass classification problem. The number of observations for each class is balanced. There are observations with 4 input variables and 1 output variable. The variable names are as follows:. Download the dataset and save it into your current working directory with the filename iris. First we will develop each piece of the algorithm in this section, then we will tie all of the elements together into a working implementation applied to a real dataset in the next section. These steps will provide the foundation that you need to implement Naive Bayes from scratch and apply it to your own predictive modeling problems. Note : This tutorial assumes that you are using Python 3. If you need help installing Python, see this tutorial:. Note : if you are using Python 2. We will need to calculate the probability of data by the class they belong to, the so-called base rate. This means that we will first need to separate our training data by class. A relatively straightforward operation. We can create a dictionary object where each key is the class value and then add a list of all the records as the value in the dictionary. It assumes that the last column in each row is the class value. Running the example sorts observations in the dataset by their class value, then prints the class value followed by all identified records.
Naive Bayes Algorithm in 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. 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 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. 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.
Naive Bayes Classifier: Learning Naive Bayes with Python
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. 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:. Your version should be the same or higher. If not, you must upgrade your version of the scikit-learn library. Stacking is provided via the StackingRegressor and StackingClassifier classes. Both models operate the same way and take the same arguments.
Naive Bayes for text classification in Python
Please cite us if you use the software. Click here to download the full example code or to run this example in your browser via Binder. In the first column, first row the learning curve of a naive Bayes classifier is shown for the digits dataset. Note that the training score and the cross-validation score are both not very good at the end. However, the shape of the curve can be found in more complex datasets very often: the training score is very high at the beginning and decreases and the cross-validation score is very low at the beginning and increases. We can see clearly that the training score is still around the maximum and the validation score could be increased with more training samples. The plots in the second row show the times required by the models to train with various sizes of training dataset. The plots in the third row show how much time was required to train the models for each training sizes. Total running time of the script: 0 minutes 3. Gallery generated by Sphinx-Gallery. Toggle Menu. Prev Up Next. Plotting Learning Curves. Note Click here to download the full example code or to run this example in your browser via Binder. Parameters estimator : object type that implements the "fit" and "predict" methods An object of that type which is cloned for each validation. Possible inputs for cv are: - None, to use the default 5-fold cross-validation, - integer, to specify the number of folds. If the dtype is float, it is regarded as a fraction of the maximum size of the training set that is determined by the selected validation methodi. Otherwise it is interpreted as absolute sizes of the training sets. Note that for classification the number of samples usually have to be big enough to contain at least one sample from each class.