Bayesian Algorithm:
Bayesian Algorithm is a classification technique support by Bayes’ Theorem with associate degree assumption of independence among predictors. In easy terms, a Naïve Bayes categorize assumes that the presence of a specific feature in an exceedingly class is unrelates to the presence of the other feature.
For example, the fruit could also be thought of to be associate degree apple if it’s red, round, and concerning three inches in diameter. Even though these options depend upon one another or the existence of the opposite options, all of those properties severely contribute to the chance that this fruit is associate degree apple which is why Bayesian Algorithm refers ‘Naïve’.
Naïve Bayes Model
Naïve Bayes model is straightforward to make and notably helpful for terribly giant information sets. together with simplicity, Naïve Bayes is understand to crush even extremely refined classification strategies.
Bayes theorem provides how of calculative posterior chance P(c|x) from P(c), P(x), and P(x|c). verify the equation below:
Above,
P(c|x) is that the posterior chance of sophistication (c, target) given predictor (x, attributes).
P(c) is the previous chance of sophistication.
P(x|c) is that the chance that is that the chance of the predictor given category.
P(x) is the previous chance of the predictor.
What are the pros and Cons of Naive Bayes?
Pros:
It is easy and fast to predict the class to take a glance at the info set. It together performs well in multi-class prediction
When the assumption of independence holds, a Naive Thomas Bayes classifier performs higher compared to various models like provision regression and you want less work info.
It performs well simply just in the case of categorical input variables compared to a numerical variable(s). For a numerical variable, distribution is assume (bell curve, which will be a sturdy assumption).
Cons:
If a categorical variable incorporates a category (in taking a glance at the info set), that wasn’t discover in the work info set, then the model will assign a zero (zero) likelihood and may unable to make a prediction. Typically| This will be} typically brought up as “Zero Frequency”. To resolve this, we tend to square measure able to use the smoothing technique. one in every of the simplest smoothing techniques is termed uranologist estimation.
On the alternative facet, naive Thomas Bayes is, also, brought up as a nasty calculator, so the prospect outputs from predict_proba are not to be taken too seriously.
Another limitation of Naive Thomas Bayes is that the belief in freelance predictors. In-universe, it’s just about unattainable that we tend to induce a gaggle of predictors that are totally freelance.
Applications of Naïve Bayes Algorithms
Real-time Prediction: Naïve Thomas Bayes is AN eager learning classifier and it’s positive quick. Thus, it can be in use for creating predictions in real-time.
Multi category Prediction: This algorithmic program is additionally document for the multi-category prediction feature. Here we will predict the likelihood of multiple categories of the target variable.
Text categorification/ Spam Filtering/ Sentiment Analysis: Naïve Thomas Bayes classifiers largely utilized in text classification (due to raised end in multi-class issues and independence rule) have a higher success rate as compared to alternative algorithms. As a result, it’s wide utilize in Spam filtering (identify spam email) and Sentiment Analysis (in social media analysis, to spot positive and negative client sentiments)
Recommendation System: Naïve Thomas Bayes Classifier and cooperative Filtering along builds a Recommendation System that uses machine learning and data processing techniques to filter unseen data and predict whether or not a user would love a given resource or not
Gaussian Naïve Bayes
The general term Naïve Thomas Bayes refers to the independence assumptions within the model, instead of the actual distribution of every feature. Up to the current purpose we’ve got aforesaid nothing regarding the distribution of every feature, however in mathematician Naïve Thomas Bayes, we tend to assume that the distribution of likelihood is a mathematician (normal). attributable to the idea of the traditional distribution.
however, mathematician Naïve Thomas Bayes is employ in cases once all our options are continuous. for instance, if we tend to think about the Iris dataset, the options are floral leaf dimension, floral leaf dimension, etc. they will have completely different values within the dataset like dimension and length, thence we tend to can’t represent them in terms of their occurrences and that we have to be compel to use the mathematician Naïve Thomas Bayes here.
Written By: Pramod Panigrahi
Reviewed By: Rushikesh Lavate
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