A canonical Frobenius structure by Douai A.

By Douai A.

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This procedure will go on until the information allows us to identify the final machine learning solution. In addition, most of the algorithms can be tuned to improve their performance, and the tuning depends on the results. The target of a new step is based on the analysis of the previous one, and sometimes, a step is modified on the basis of the following results. There isn't a well-defined path that goes from the start to the end, and the software should allow that. The software should, at the same time, provide the user with the machine learning tools and allow specific solutions to be built.

9 ... 1 ... 5 ... 1 ... : 1 1 1 1 1 1 1 1 1 1 ... data: This is the data frame. Each chunk corresponds to a possible value of the variable. fun: This is the function to apply to each chunk. There are other ply functions and the options are: a: Array d: Data frame l: List For instance, adply defines a data frame starting from an array, and laply defines an array starting from a list. There are more than 5,000 packages in the CRAN repository, and we will see a few of them in the following chapters.

In this chapter, we will use an interesting dataset about the Titanic passengers. First, we can explore some data in order to understand what happened. Then, starting from the personal data of other passengers, the goal of the machine learning model is forecasting which new passengers will survive. Unfortunately, its syntax is more complex than the basic R charts, so we don't have enough space for it in this book. We can see the format of data using class: class(Titanic) [1] "table" The object, Titanic, belongs to the table class so it displays the count of each combination of categoric variables, as shown: Titanic , , Age = Child, Survived = No Sex Class Male Female 1st 0 0 2nd 0 0 3rd 35 17 Crew 0 0 ...

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