Introduction to Naïve Bayes
It is a classification technique based on Bayes’ Theorem with an assumption of independence among predictors. Naive Bayes model is easy to build and particularly useful for very large datasets. Along with simplicity, Naive Bayes is known to outperform even the most-sophisticated classification methods. It proves to be quite robust to irrelevant features, which it ignores. It learns and predicts very fast and it does not require lots of storage. So, why is it then called naive? The naive was added to the account for one assumption that is required for Bayes to work optimally: all features must be independent of each other. In reality, this is usually not the case; however, it still returns very good accuracy in practice even when the independent assumption does not hold.
4 Applications of Naive Bayes Algorithms
Below are the common applications of Naive Bayes Algorithms:
- Real-time Prediction: As Naive Bayes is super fast, it can be used for making predictions in real time.
- Multi-class Prediction: This algorithm can predict the posterior probability of multiple classes of the target variable.
- Text classification/ Spam Filtering/ Sentiment Analysis: Naive Bayes classifiers are mostly used in text classification (due to their better results in multi-class problems and independence rule) have a higher success rate as compared to other algorithms. As a result, it is widely used in Spam filtering (identify spam e-mail) and Sentiment Analysis (in social media analysis, to identify positive and negative customer sentiments)
- Recommendation System: Naive Bayes Classifier along with algorithms like Collaborative Filtering makes a Recommendation System that uses machine learning and data mining techniques to filter unseen information and predict whether a user would like a given resource or not.
Mathematical Overview (Probability model):
Naive Bayes methods are a set of supervised learning algorithms based on applying Bayes’ theorem with the “naive” assumption of independence between every pair of features. Given a class variable y and a dependent feature vector x1 through xn, Bayes’ theorem states the following relationship:
We now have a relationship between the target and the features using Bayes Theorem along with a Naive Assumption that all features are independent.
Constructing the NB Classifier from the Probability model:
So far we have derived the independent feature model, that is, the Naive Bayes probability model. The Naive Bayes classifier combines this model with a decision rule; this decision rule will decide which hypothesis is most probable. Picking the hypothesis that is most probable is known as the maximum a posteriori or MAP decision rule. The corresponding classifier, a Bayes classifier, is the function that assigns a class label to y as follows:
Since P(x1, …, xn) is constant given the input, we can use the following classification rule:
We can use Maximum A Posteriori (MAP) estimation to estimate P(y) and P(xi | y); the former is then the relative frequency of class y in the training set.
There are different naive Bayes classifiers that differ mainly by the assumptions they make regarding the distribution of P(xi | y). When dealing with continuous data, a typical assumption is that the continuous values associated with each class are distributed according to a Gaussian distribution. So we will use the Gaussian Naïve Bayes.
Problem Statement:
To build a simple generative classification model called Naive Bayes for predicting the quality of the car given few of other car attributes.
Data details
========================================== 1. Title: Car Evaluation Database ========================================== The dataset is available at “http://archive.ics.uci.edu/ml/datasets/Car+Evaluation” 2. Sources: (a) Creator: Marko Bohanec (b) Donors: Marko Bohanec ([email protected]) Blaz Zupan ([email protected]) (c) Date: June, 1997 3. Past Usage: The hierarchical decision model, from which this dataset is derived, was first presented in M. Bohanec and V. Rajkovic: Knowledge acquisition and explanation for multi-attribute decision making. In 8th Intl Workshop on Expert Systems and their Applications, Avignon, France. pages 59-78, 1988. Within machine-learning, this dataset was used for the evaluation of the HINT (Hierarchy INduction Tool), which was proved to be able to completely reconstruct the original hierarchical model. This, together with a comparison with C4.5, is presented in B. Zupan, M. Bohanec, I. Bratko, J. Demsar: Machine learning by function decomposition. ICML-97, Nashville, TN. 1997 (to appear) 4. Relevant Information Paragraph: Car Evaluation Database was derived from a simple hierarchical decision model originally developed for the demonstration of DEX (M. Bohanec, V. Rajkovic: Expert system for decision making. Sistemica 1(1), pp. 145-157, 1990.). The model evaluates cars according to the following concept structure: CAR car acceptability PRICE overall price buying buying price maint price of the maintenance TECH technical characteristics COMFORT comfort doors number of doors persons capacity in terms of persons to carry lug_boot the size of luggage boot safety estimated safety of the car Input attributes are printed in lowercase. Besides the target concept (CAR), the model includes three intermediate concepts: PRICE, TECH, COMFORT. 5. Number of Instances: 1728 (instances completely cover the attribute space) 6. Number of Attributes: 6 7. Attribute Values: buying v-high, high, med, low maint v-high, high, med, low doors 2, 3, 4, 5-more persons 2, 4, more lug_boot small, med, big safety low, med, high 8. Missing Attribute Values: none 9. Class Distribution (number of instances per class) class N N[%] ----------------------------- unacc 1210 (70.023 %) acc 384 (22.222 %) good 69 ( 3.993 %) v-good 65 ( 3.762 %)
Tools to be used:
Numpy, pandas, scikit-learn
Python Implementation with code:
1. Import necessary libraries
Import the necessary modules from specific libraries.
import os import numpy as np import pandas as pd import numpy as np, pandas as pd import matplotlib.pyplot as plt from sklearn import metrics , model_selection ## Import the Classifier. from sklearn.naive_bayes import GaussianNB
2. Load the data set
Use the pandas module to read the bike data from the file system. Check few records of the dataset.
data =
pd.read_csv('data/car_quality/car.data',names=['buying','maint','doors','persons','lug_boot','safety','class'])
data.head()
buying maint doors persons lug_boot safety class
0 vhigh vhigh 2 2 small low unacc
1 vhigh vhigh 2 2 small med unacc
2 vhigh vhigh 2 2 small high unacc
3 vhigh vhigh 2 2 med low unacc
4 vhigh vhigh 2 2 med med unacc
3. Check a few information about the data set
data.info() <class 'pandas.core.frame.DataFrame'> RangeIndex: 1728 entries, 0 to 1727 Data columns (total 7 columns): buying 1728 non-null object maint 1728 non-null object doors 1728 non-null object persons 1728 non-null object lug_boot 1728 non-null object safety 1728 non-null object class 1728 non-null object dtypes: object(7) memory usage: 94.6+ KB
The train dataset has 1728 rows and 7 columns.
There are no missing values in the dataset
4. Identify the target variable
data['class'],class_names = pd.factorize(data['class'])
The target variable is marked as a class in the data frame. The values are present in string format. However, the algorithm requires the variables to be coded into its equivalent integer codes. We can convert the string categorical values into an integer code using factorize method of the pandas library.
Let’s check the encoded values now.
print(class_names) print(data['class'].unique()) Index([u'unacc', u'acc', u'vgood', u'good'], dtype='object') [0 1 2 3]
The values have been encoded into 4 different numeric labels.
5. Identify the predictor variables and encode any string variables to equivalent integer codes
data['buying'],_ = pd.factorize(data['buying']) data['maint'],_ = pd.factorize(data['maint']) data['doors'],_ = pd.factorize(data['doors']) data['persons'],_ = pd.factorize(data['persons']) data['lug_boot'],_ = pd.factorize(data['lug_boot']) data['safety'],_ = pd.factorize(data['safety']) data.head() buying maint doors persons lug_boot safety class 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 2 0 0 0 0 0 2 0 3 0 0 0 0 1 0 0 4 0 0 0 0 1 1 0
Check the data types now:
data.info() <class 'pandas.core.frame.DataFrame'> RangeIndex: 1728 entries, 0 to 1727 Data columns (total 7 columns): buying 1728 non-null int64 maint 1728 non-null int64 doors 1728 non-null int64 persons 1728 non-null int64 lug_boot 1728 non-null int64 safety 1728 non-null int64 class 1728 non-null int64 dtypes: int64(7) memory usage: 94.6 KB
Everything is now converted to integer form.
Select the predictor feature and the target variable
X = data.iloc[:,:-1] y = data.iloc[:,-1]
6. Train test split:
# split data randomly into 70% training and 30% test X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.3, random_state=123)
7. Training/model fitting
model = GaussianNB() ## Fit the model on the training data. model.fit(X_train, y_train)
8. Model parameters study :
# use the model to make predictions with the test data
y_pred = model.predict(X_test)
# how did our model perform?
count_misclassified = (y_test != y_pred).sum()
print('Misclassified samples: {}'.format(count_misclassified))
accuracy = metrics.accuracy_score(y_test, y_pred)
print('Accuracy: {:.2f}'.format(accuracy))
Misclassified samples: 150
Accuracy: 0.71
Algorithm Advantages:
- It is easy to apply and predicts the class of test data set fast. It also performs well in multi-class prediction
- When the assumption of independence holds, a Naive Bayes classifier performs better compared to the other models like logistic regression as you need less training data.
- It performs well in the case of categorical input variables compared to a numerical variable(s). For the numerical variable, a normal distribution is assumed (bell curve, which is a strong assumption).
Algorithm Disadvantages:
- If the categorical variable has a category (in test data set), which was not observed in training data set, then the model will assign a 0 (zero) probability and will be unable to make a prediction. This is often known as “Zero Frequency”. To solve this, we can use the smoothing technique. One of the simplest smoothing techniques is called Laplace estimation.
- On the other side naive Bayes is also known as a bad estimator, so the probability outputs from predict_proba are not to be taken too seriously.
- Another limitation of Naive Bayes is the assumption of independent predictors. In real life, it is almost impossible to get a set of predictors which are completely independent.
This model has an accuracy score of 71% since this is a very simplistic dataset with distinctly separable classes. That’s how to implement Naive-Bayes with scikit-learn. Load your favorite dataset and give it a try!. From here on, all you need is practice.
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