There is a famous problem that was presented in the American television game show Let's Make a Deal. In the show there is a part where the host presents a probability puzzle for the contestant. What makes this puzzle interesting is that the answer is a bit counter-intuitive at first. Yet, even when explained and proven, some still are not convinced by the answer. This problem is the Monty Hall Problem.
With this post I will show a way to get the correct answer to the puzzle by implementing it. Then simulating the puzzle enough times to have an answer without complicated math.
Machine learning is becoming more and more ubiquitous in our daily lives. From thermostats which know when you will be home, to smarter cars and the phone we have in our pocket. It seems like it is everywhere and that makes it an interesting field to explore! But what is machine learning? In general terms, it is a way for a system to learn and make predictions. This can be as simple as predicting relevant shopping items to as complex as a digital assistant.
With this blog post I will try to give an introduction into classification using the Naive Bayes classifier algorithm. It is an easy algorithm to implement while giving decent results. But it will need some statistic knowledge to understand, so bear with me. By the end of it you might see some applications and even try to implement it yourself!