Hands-on Deep Learning with R by Michael Pawlus
Author:Michael Pawlus [Michael Pawlus]
Language: eng
Format: epub
Tags: COM004000 - COMPUTERS / Intelligence (AI) and Semantics, COM037000 - COMPUTERS / Machine Theory, COM018000 - COMPUTERS / Data Processing
Publisher: Packt Publishing
Published: 2020-04-24T05:03:54+00:00
ohe <- caret::dummyVars(" ~ .", data = train_chars)
train_ohe <- data.frame(predict(ohe, newdata = train_chars))
After running the preceding code, we will have two new data objects in our Environment pane. One is the ohe object, which is a list with all the details that we need to convert our string columns to dummy variables, and the other is the train_ohe object, which contains the dummy variables. Your Environment pane will now look as follows:
We can see that creating dummy variables results in a dataset with many more columns than our original data. As stated, we take every column name and value pair and create a new column, which results in the growth of columns.
After the columns containing string values have been converted to columns with numeric values, then we can column-bind both subsets back together again. We combine the data that was already numeric and the data that was converted to a numeric format using the following line of code:
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Computer Vision & Pattern Recognition | Expert Systems |
Intelligence & Semantics | Machine Theory |
Natural Language Processing | Neural Networks |
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