Naive Bayes: Implementation from scratch.

  • Naive means that the features are independent of each other and the inference of anything will be contributed of all features.
  • Bayes is from the Bayes theorem .
Image Source Google.
  • P(A|B): Posterior probability.
  • P(A): Prior probability.
  • P(B|A): Likelihood.
  • P(B): Evidence.
  • prior_probability(): Probability of hypothesis before observing the evidence.
  • statistics(): This calculates mean, variance which needed for gaussian distribution for each column and convert to numpy array
  • gaussian_density(): This is the Gaussian Distribution here u signifies the mean, sigma is the standard deviation and sigma² is the variance statistics() function feeds input to it.
  • posterior_probability(): This calculates P(A) Probability of hypothesis A on the observed event B.
  • fit(): This functions fits our Naive Bayes Model.
  • predict(): This function provides predictions.
  • accuracy():




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Akshar Rastogi

Akshar Rastogi

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