Machine Learning in Python Essential Techniques for Predictive Analysis by Michael Bowles (z-lib.org).epub

facilitate iterating through the process of training and tweaking. If the
data set grows to 1,000 x 1,000, the training times will grow to a
fraction of a minute for penalized linear regression and a few minutes
for an ensemble method. As the data set gets to several tens of
thousands of rows and columns, the training times will expand to 3 or
4 hours for penalized linear regression and 12 to 24 hours for an
ensemble method. The larger training times will have an impact on
your development time because you’ll iterate a number of times.
The second important observation regarding row and column counts
is that if the data set has many more columns than rows, you may be
more likely to get the best prediction with penalized linear regression
and vice versa. Chapter 3, “Predictive Model Building: Balancing
Performance, Complexity, and Big Data,” and the examples you’ll
run later will give you a better understanding of why that’s true.
The next step in the checklist is to determine how many of the
columns of data are numeric versus categorical. Listing 2-2 shows
code to accomplish this for the rocks versus mine data set. The code
runs down each column and adds up the number of entries that are
numeric (int or float), the number of entries that are nonempty
strings, and the number that are empty. The result is that the first 60
columns contain all numeric values and the last column contains all
strings. The string values are the labels. Generally, categorical
variables are presented as strings, as in this example. In some cases,
binary-valued categorical variables are presented as a 0,1 numeric
variable.