Classification Algorithm: Naive Bayes

Naive Bayes calculates the probability of each class and the conditional probability(the probability of an event ‘A’, given that another ‘B’ has already occurred) of each class which is then used by Bayes Theorem to make predictions.

Gaussian distribution is assumed so that you can easily estimate these probabilities.

Note: Naive Bayes is a non-linear machine learning(ML) algorithm. It’s called naive because it assumes that each input variable is independent even though it is unrealistic for real data. But it is still very effective.

Medium Post: Top 10 algorithms for ML newbies

This recipe includes the following topics:

  • Load classification problem dataset (Pima Indians) from github
  • Split columns into the usual feature columns(X) and target column(Y)
  • Set k-fold count to 10
  • Set seed to reproduce the same random data each time
  • Split data using KFold() class
  • Instantiate the classification algorithm: GaussianNB
  • Call cross_val_score() to run cross validation
  • Calculate mean estimated accuracy from scores returned by cross_val_score()

# import modules
import pandas as pd
import numpy as np
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score

# read data file from github
# dataframe: pimaDf
gitFileURL = ''
cols = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
pimaDf = pd.read_csv(gitFileURL, names = cols)

# convert into numpy array for scikit-learn
pimaArr = pimaDf.values

# Let's split columns into the usual feature columns(X) and target column(Y)
# Y represents the target 'class' column whose value is either '0' or '1'
X = pimaArr[:, 0:8]
Y = pimaArr[:, 8]

# set k-fold count
folds = 10

# set seed to reproduce the same random data each time
seed = 7

# split data using KFold
kfold = KFold(n_splits=folds, random_state=seed)

# instantiate the classification algorithm
model = GaussianNB()

# call cross_val_score() to run cross validation
resultArr = cross_val_score(model, X, Y, cv=kfold)

# calculate mean of scores for all folds
meanAccuracy = resultArr.mean() * 100

# display mean estimated accuracy
print("Mean estimated accuracy: %.3f%%" % meanAccuracy)
Mean estimated accuracy: 75.518%

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