In the following series of blogs, I will make some notes about the book “Machine Learning with R” written by Brett Lantz. In this blog, I will start with the introduction of R and Data Management.
WorkFlow in ML
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Machine learning algorithms are virtually a prerequisite for data mining but the opposite is not true.
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Trying to model the noise in data is the basis of the overfitting problem.
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Collecting data -> Exploring and preparing the data -> Trainga model on the data -> Evaluating model performance -> Improving model performance.
Data Structure in R
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Vector, all elements contained in a vector must be of the same type; ‘NA’ means missing value; ‘NULL’ means no value; other types include integer, numeric, character, and logical;
subject_name <- c("Jason","Tom","Bob"); -
Lists, similar to vectors, can be understood as structures that allow the collection of different types of values; lists are created using the list() function;
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Data frame, which combines the characteristics of vectors and lists, columns represent attributes, and rows represent instances, using the data.frame() function;
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Factor, an attribute that represents a feature with a category value is called a nominal attribute. In R, the factor() function is applied to represent this attribute data; levels specify all the categories that the data may obtain;
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Matrix, which can only contain a single type of data;
m <- matrix(c('a','b','c','d'), nrow=2);
Some Functions in R
I just noted some main key functions in R for exploring the statistical features of Data, and the most important things are learning to read the manual and using Google.
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The function
str()provides a method to display the structure of the data frame; -
The function
summary()provides statistics such as min, 1st Qu, Median, Mean, 3rd Qu, and Max; -
The function
range()returns the max and min values,diff()means difference,IQRget the interquartile range;quantile()finds any quantile; andseq()produces the vector of the same interval size elements;