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Ethan
Ethan
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Naive Bayes classifiers have the following characteristics-:

  1. They are robust to isolated noise points because such points are averaged out when estimating contiditional probabilities from data. Naive Bayes classifiers can also handle missing values by ignoring the example during model building and classification.

  2. They are robust to irrelevant attributes. If X_i is an irrelevant attributet then P(X_i/Y) becomes almost uniformly distributed. The class conditional probability for X_i has no impact on overall computation of posterior probability.

I barely understand anything said here. BookThe book doesn't even provide examples. And and most of the resources available in the internetonline are exact photocopyphotocopies of thethis book. None of those materials dive deep into these things and actually explain this.

Can you guys help me out here to explain what this meanmeans via examples.? I will be really glad. I have been banging my head inagainst the wall to get this concept sincefor a long time. I will be glad with some recommended reading that I need to do as well.

Naive Bayes classifiers have the following characteristics-:

  1. They are robust to isolated noise points because such points are averaged out when estimating contiditional probabilities from data. Naive Bayes classifiers can also handle missing values by ignoring the example during model building and classification.

  2. They are robust to irrelevant attributes. If X_i is an irrelevant attributet then P(X_i/Y) becomes almost uniformly distributed. The class conditional probability for X_i has no impact on overall computation of posterior probability.

I barely understand anything said here. Book doesn't even provide examples. And most of the resources available in the internet are exact photocopy of the book. None of those materials dive deep into these things and actually explain this.

Can you guys help me out here to explain what this mean via examples. I will be really glad. I have been banging my head in the wall to get this concept since a long time. I will be glad with some recommended reading that I need to do as well.

Naive Bayes classifiers have the following characteristics-:

  1. They are robust to isolated noise points because such points are averaged out when estimating contiditional probabilities from data. Naive Bayes classifiers can also handle missing values by ignoring the example during model building and classification.

  2. They are robust to irrelevant attributes. If X_i is an irrelevant attributet then P(X_i/Y) becomes almost uniformly distributed. The class conditional probability for X_i has no impact on overall computation of posterior probability.

I barely understand anything said here. The book doesn't even provide examples and most of the resources available online are exact photocopies of this book. None of those materials dive deep into these things and actually explain this.

Can you guys help me out here to explain what this means via examples? I will be really glad. I have been banging my head against the wall to get this concept for a long time. I will be glad with some recommended reading that I need to do as well.

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achhainsan
achhainsan
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Really confused with characteristics of Naive Bayes classifiers?

Naive Bayes classifiers have the following characteristics-:

  1. They are robust to isolated noise points because such points are averaged out when estimating contiditional probabilities from data. Naive Bayes classifiers can also handle missing values by ignoring the example during model building and classification.

  2. They are robust to irrelevant attributes. If X_i is an irrelevant attributet then P(X_i/Y) becomes almost uniformly distributed. The class conditional probability for X_i has no impact on overall computation of posterior probability.

I barely understand anything said here. Book doesn't even provide examples. And most of the resources available in the internet are exact photocopy of the book. None of those materials dive deep into these things and actually explain this.

Can you guys help me out here to explain what this mean via examples. I will be really glad. I have been banging my head in the wall to get this concept since a long time. I will be glad with some recommended reading that I need to do as well.