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Encoding in writing
Encoding in writing








encoding in writing

When working with large sets of categorical features, One-Hot can turn a 5 column dataframe into a 50 column dataframe, which is incredibly hard to work with! On top of that, typically with higher stakes in categories, One-Hot’s effectiveness may drop dramatically. Additionally, One-Hot really shines in this exact light. This is because first and foremost, One-Hot-Encoded data takes up a lot of memory and disk space compared to the other algorithms available. Typically, I use One-Hot in situations where I have as few categories as possible.

encoding in writing

One-Hot-Encoding, also called One-Hot, or Dummy-Encoding takes a very radical approach to dealing with categorical variables. If you’re new to machine-learning, one trick you should definitely snatch up as soon as possible is the ability to One-Hot-Encode a Data-Frame. Without further ado, let’s look at some encoders! Typically, whenever machine-learning is being done with strings, a Data-Scientist will be working with an encoder. Asking a computer to interpret words, especially sentences with subjective meaning or emotion is like having the Cookie monster eat celery įortunately, there is a solution to this problem - there are many different ways that you can approach turning words into numbers for analysis! Though doing so might not allow a computer to analyze certain things about words, it can certainly help with solving common machine-learning problems that you may encounter in the educational grind that is Data-Science. Computers speak quantitatively, rather than qualitatively. The problem with the combination of data and strings and words is that words cannot directly be analyzed by an artificial brain.

encoding in writing

As a result, it is unfortunately incredibly common to come across words (or “ strings” in “ beep boop” language) rather than numbers when working with data-sets, and this is even true of the cleanest data-sets available today. Unfortunately, humans went and developed the phonetic alphabet before they started talking in binary, or “ beep boop” speech. In a perfect world, all programmers, scientists, data-engineers, analysts, and machine-learning engineers alike dream that all data could arrive at their doorstep in the cleanest form possible.










Encoding in writing