I have already gone over the recursive solution. It was simple, maybe even elegant. However, its complexity was horrendous, factorial growth. The solution I’m about to lay out is complex in comparison. So we should do a simple example first.
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Just by looking at formulas for Combinations (Binomial Coefficients), we are potentially talking about taking 1 astronomically large number and dividing it by two other not so astronomically large, just stupidly large, numbers. All that, just to get a number that is quite small in comparison.
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So recently YouTube suggested that I watch Amazon Coding Interview Question – Recursive Staircase Problem. The recursive solution to this problem is quite elegant and is what this post will cover. However, there is a much faster solution, which I will cover in a later post.
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This is my first attempt at text classification. I wanted to make it be “Binomial”, and it turned out better than I was expecting.
This is my first attempt at text classification. I wanted to make it be “Binomial”, but I had to really force it. The results ended up being good, so my forcing can’t be completely bad.
I was looking on UC Irvine Machine Learning Repository for a data set to perform either Binomial or Multinomial Bayes on. What I found was a data set that I could just use simple probability on. I wish I would have found this one before I did Gaussian.
Let’s apply Naive Bayes to the Iris Flower Data Set.
A Naive Bayes Classifier is a supervised Machine Learning algorithm that predicts the class of new samples by utilizing Baye’s Theorem.
Bayes anything can be considered buzz words in practically any environment. I want to go on a journey to demystifying some of this buzziness and land on some real understanding.
Conditional probability is soo powerful. Powerful in that there is a difference in the likelihood of someone developing breast cancer based on family history, lifestyle, genetics, if they are a man, or if they are women. There is math behind those statements!
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