polyfit function uses polynomial regression to predict a smooth, non-linear curve through a bi-variate scatter plot. The
polyfit function takes three parameters:
The numeric field containing the independent (x) variable
The numeric field containing the dependent (y) variable
The integer degree of the polynomial
The degree determines the number of curves to the fitted curve. One degree polynomial performs linear regression. Typically the degree is an integer from 1 to 5.
select polyfit(petal_length_d, petal_width_d, 3) as prediction, residual, petal_length_d, petal_width_d from iris limit 150
The result set contains a random sample of records that match the
WHERE clause. If no
WHERE clause is included the random sample will be taken from the entire result set. The size of the result set can be controlled by the
LIMIT clause. The default size, if no limit is applied, is 25000.
polyfit function returns the predicted value for each record. There are three additional fields that can be selected when the
polyfit function is used:
residual: the residual value for each sample
The residual value is the samples dependent (y) value minus the predicted value. The residual represents the error of the regression prediction for each sample.
the independent variable for each sample
the dependent variable for each sample
There are a number of visualizations that can flow from the regression result set.
The first visualization shown is a scatter plot with
petal_length_d on the x-axis and
petal_width_d on the y-axis. This can be used to visualize the relationship between the two variables in the regression analysis.
The second visualization shows the
petal_length_d variable on the x-axis and the prediction for
petal_width_d on y-axis.
The last visualization plots the predictions on the x-axis and the residual on the y-axis. This residual plot can be used to visualize the error of the regression model across the full range of predictions.