Learning From Data - Homework 2 - A solution in LIONoso
Courtesy of Giovanni Pellegrini
Among the exercises of the second week, we provide a solution of the exercises
about Linear Regression (exercise 5, 6, 7).
The script is written in Python and it uses the numpy module
Before proceeding, be sure to have Python and numpy installed on your computer.
Connecting the Linear Regression Python script to LIONoso
You can download the Linear Regression Algorithm script here,
containing our solution.
Please see the notes for Windows users
below if the script doesn't work on Windows.
You can load the script by dragging a Parametric
table element into the workbench, and by specifying the filename of your script.
In the above figure we just loaded the script.
Depending on the script content, in the left panel you can specify the
parameter values, in our case the number of experiments to
perform (default 1000), the dimension of the training set for Linear Regression (default
100) and the dimension of training set for the Perceptron (default 10).
By clicking the "Compute" button, the script is launched and a table containing the results of each experiment
The output of every experiment contains 4 columns:
Table Row number, In sample error, Out of sample error, Number of iterations the perceptron needs to converge.
To compute the average of each parameter you can:
Open a Bubblechart from the output table (right click on the table generated, select "New panel"->"Bubble"),
drag the "Ein" column
onto the y axis. Then select the "Advanced properties" tab in the left panel and select "Show polynomial fit" with 0 degree.
A red line will appear on the plot showing the average value.
Now repeat for the other two columns, drag and fit them.
To zoom on a specific part of the chart, just select the region.
The results of the tests (1000 tests, 100 points for Linear Training, 10 points for Perceptron Training) are:
Iterations of the perceptron: