Machine Learning “for Dummies” (part 3) : The Decision tree and Decision Forest algorithms

In the last two posts (Machine Learning “for Dummies” [part 1] and Machine Learning “for Dummies” [part 2]) we introduced what was the “Classification” of Machine Learning. We also presented a very famous algorithm called “k-nearest neighbors”. If you have not read these posts, you may want to do it now to be sure to understand this post.

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Machine Learning “for Dummies” (part 2) : The K-nearest neighbors algorithm

In the last post, we introduced what was the « Classification » of Machine Learning. We also presented some examples of applications (banks, hospitals, etc.) and we started to play with the Iris dataset. If you have not read the first post, now would be a good time in order to fully understand the following article.

Back to the Iris story: Remember, we have a list of iris flowers described by the length and width of their petals and sepals (the attributes). Also, we know the species of all except one iris (we will call this iris the “mysterious iris”), and we would like to find the species (or class) of the mysterious iris. For this task, we suppose the attributes of the irises to be indicative of the species.

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Machine Learning for Dummies (part 1) : Introduction to Classification

You might have already heard about the terms “Machine Learning” (or “Data Mining”, or “Big data”, or Data Analysis, or Data Science, or Cloud Computing) but you never actually knew what they meant. Maybe you know those are computer or mathematical stuffs and that is all. Maybe you have to use some softwares that relies on Machine Learning in your job, but for you, it is like magic.

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