AI News, Machine Learning Basics with Naive Bayes

Machine Learning Basics with Naive Bayes

After researching and looking into the different algorithms associated with Machine Learning, I’ve found that there is an abundance of great material showing you how to use certain algorithms in a specific language.

The algorithm then uses this combo of data item and outcome/answer in order to “learn” what sorts of things dictate a certain answer.

When provided with data it has never seen before, that isn’t labelled, this trained model can then predict the answer based on what it has seen before.

For example, given a set of emails and people that wrote them, Naive Bayes can be used to build a model to understand the writing styles of each email author.

I’ve taken the Kaggle Simpsons data set and used the script and character data to try and train a machine learning model, using Naive Bayes, to predict whether it was Homer or Bart that said a certain phrase.

To get the main bulk of the code that would help you vectorise the phrases and preprare them into a training and test data set, see the Udacity Intro to Machine Learning Github repo and take a look at their Naive Bayes examples.

Firstly, filter and split your Simpsons data up - you can do this manually - to get a file that contains one id on every line that is either a Bart id (8) or a Homer id (2).

Make another file and put the normalised text for this filtered data on each line (make sure its in the same order as the id’s so row 1’s id matches row 1’s text etc…).

You can now add further Bart and Homer id’s from the data set (as there were multiple for their different characters) and start tweaking parameters to see if you can improve the accuracy.

6 Easy Steps to Learn Naive Bayes Algorithm (with codes in Python and R)

Here’s a situation you’ve got into: You are working on a classification problem and you have generated your set of hypothesis, created features and discussed the importance of variables.

In this article, I’ll explain the basics of this algorithm, so that next time when you come across large data sets, you can bring this algorithm to action.

Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.

Step 1: Convert the data set into a frequency table Step 2: Create Likelihood table by finding the probabilities like Overcast probability = 0.29 and probability of playing is 0.64.

Yes) * P(Yes) / P (Sunny) Here we have P (Sunny |Yes) = 3/9 = 0.33, P(Sunny) = 5/14 = 0.36, P( Yes)= 9/14 = 0.64 Now, P (Yes |

There are three types of Naive Bayes model under scikit learn library: Based on your data set, you can choose any of above discussed model.

Above, we looked at the basic Naive Bayes model, you can improve the power of this basic model by tuning parameters and handle assumption intelligently.

Further, I would suggest you to focus more on data pre-processing and feature selection prior to applying Naive Bayes algorithm.0 In future post, I will discuss about text and document classification using naive bayes in more detail.

Naive Bayes classifier

In machine learning, naive Bayes classifiers are a family of simple 'probabilistic classifiers' based on applying Bayes' theorem with strong (naive) independence assumptions between the features.

It was introduced under a different name into the text retrieval community in the early 1960s,[1]:488 and remains a popular (baseline) method for text categorization, the problem of judging documents as belonging to one category or the other (such as spam or legitimate, sports or politics, etc.) with word frequencies as the features.

With appropriate pre-processing, it is competitive in this domain with more advanced methods including support vector machines.[2]

Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predictors) in a learning problem.

Maximum-likelihood training can be done by evaluating a closed-form expression,[1]:718 which takes linear time, rather than by expensive iterative approximation as used for many other types of classifiers.

In the statistics and computer science literature, naive Bayes models are known under a variety of names, including simple Bayes and independence Bayes.[4]

Naive Bayes is a simple technique for constructing classifiers: models that assign class labels to problem instances, represented as vectors of feature values, where the class labels are drawn from some finite set.

It is not a single algorithm for training such classifiers, but a family of algorithms based on a common principle: all naive Bayes classifiers assume that the value of a particular feature is independent of the value of any other feature, given the class variable.

A naive Bayes classifier considers each of these features to contribute independently to the probability that this fruit is an apple, regardless of any possible correlations between the color, roundness, and diameter features.

For some types of probability models, naive Bayes classifiers can be trained very efficiently in a supervised learning setting.

In many practical applications, parameter estimation for naive Bayes models uses the method of maximum likelihood;

Despite their naive design and apparently oversimplified assumptions, naive Bayes classifiers have worked quite well in many complex real-world situations.

In 2004, an analysis of the Bayesian classification problem showed that there are sound theoretical reasons for the apparently implausible efficacy of naive Bayes classifiers.[5]

Still, a comprehensive comparison with other classification algorithms in 2006 showed that Bayes classification is outperformed by other approaches, such as boosted trees or random forests.[6]

An advantage of naive Bayes is that it only requires a small number of training data to estimate the parameters necessary for classification.[citation needed]

Abstractly, naive Bayes is a conditional probability model: given a problem instance to be classified, represented by a vector

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The problem with the above formulation is that if the number of features n is large or if a feature can take on a large number of values, then basing such a model on probability tables is infeasible.

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which can be rewritten as follows, using the chain rule for repeated applications of the definition of conditional probability:

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class's prior may be calculated by assuming equiprobable classes (i.e., priors = 1 / (number of classes)), or by calculating an estimate for the class probability from the training set (i.e., (prior for a given class) = (number of samples in the class) / (total number of samples)).

To estimate the parameters for a feature's distribution, one must assume a distribution or generate nonparametric models for the features from the training set.[8]

For discrete features like the ones encountered in document classification (include spam filtering), multinomial and Bernoulli distributions are popular.

When dealing with continuous data, a typical assumption is that the continuous values associated with each class are distributed according to a Gaussian distribution.

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Another common technique for handling continuous values is to use binning to discretize the feature values, to obtain a new set of Bernoulli-distributed features;

some literature in fact suggests that this is necessary to apply naive Bayes, but it is not, and the discretization may throw away discriminative information.[4]

With a multinomial event model, samples (feature vectors) represent the frequencies with which certain events have been generated by a multinomial

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This is the event model typically used for document classification, with events representing the occurrence of a word in a single document (see bag of words assumption).

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If a given class and feature value never occur together in the training data, then the frequency-based probability estimate will be zero.

Therefore, it is often desirable to incorporate a small-sample correction, called pseudocount, in all probability estimates such that no probability is ever set to be exactly zero.

discuss problems with the multinomial assumption in the context of document classification and possible ways to alleviate those problems, including the use of tf–idf weights instead of raw term frequencies and document length normalization, to produce a naive Bayes classifier that is competitive with support vector machines.[2]

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is a boolean expressing the occurrence or absence of the i'th term from the vocabulary, then the likelihood of a document given a class

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Given a way to train a naive Bayes classifier from labeled data, it's possible to construct a semi-supervised training algorithm that can learn from a combination of labeled and unlabeled data by running the supervised learning algorithm in a loop:[11]

This training algorithm is an instance of the more general expectation–maximization algorithm (EM): the prediction step inside the loop is the E-step of EM, while the re-training of naive Bayes is the M-step.

The algorithm is formally justified by the assumption that the data are generated by a mixture model, and the components of this mixture model are exactly the classes of the classification problem.[11]

Despite the fact that the far-reaching independence assumptions are often inaccurate, the naive Bayes classifier has several properties that make it surprisingly useful in practice.

In particular, the decoupling of the class conditional feature distributions means that each distribution can be independently estimated as a one-dimensional distribution.

This helps alleviate problems stemming from the curse of dimensionality, such as the need for data sets that scale exponentially with the number of features.

For example, the naive Bayes classifier will make the correct MAP decision rule classification so long as the correct class is more probable than any other class.

In the case of discrete inputs (indicator or frequency features for discrete events), naive Bayes classifiers form a generative-discriminative pair with (multinomial) logistic regression classifiers: each naive Bayes classifier can be considered a way of fitting a probability model that optimizes the joint likelihood

The link between the two can be seen by observing that the decision function for naive Bayes (in the binary case) can be rewritten as 'predict class

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The left-hand side of this equation is the log-odds, or logit, the quantity predicted by the linear model that underlies logistic regression.

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however, research by Ng and Jordan has shown that in some practical cases naive Bayes can outperform logistic regression because it reaches its asymptotic error faster.[13]

The classifier created from the training set using a Gaussian distribution assumption would be (given variances are unbiased sample variances):

This prior probability distribution might be based on our knowledge of frequencies in the larger population, or on frequency in the training set.

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Note that a value greater than 1 is OK here – it is a probability density rather than a probability, because height is a continuous variable.

Imagine that documents are drawn from a number of classes of documents which can be modeled as sets of words where the (independent) probability that the i-th word of a given document occurs in a document from class C can be written as

(For this treatment, we simplify things further by assuming that words are randomly distributed in the document - that is, words are not dependent on the length of the document, position within the document with relation to other words, or other document-context.)

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the case of two mutually exclusive alternatives (such as this example), the conversion of a log-likelihood ratio to a probability takes the form of a sigmoid curve: see logit for details.)

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