by: Chris Cave
(Feyn version 3.0 or newer)
Feyn offers a range of tools to help you dissect your
As sample data we are going for the boston housing price prediction dataset from sklearn where we are predicting median house prices of different areas around Boston. Below we import data, prepare it and find a good
Model from a
import feyn from sklearn.datasets import load_boston import pandas as pd from feyn.tools import split #Download boston housing dataset boston = load_boston() df_boston = pd.DataFrame(boston.data, columns=boston.feature_names) df_boston['PRICE'] = boston.target # Train/test split train, test = split(df_boston) # Instantiate a QLattice ql = feyn.QLattice() models = ql.auto_run( data=train, output_name='PRICE' ) # Select the best Model best = models
One of the basic diagnosics we can do with a
Model is to plot the residuals (
y_pred). This can help analyse whether errors are normally distributed or not. If they have an unusual distribution then it points towards biases in the
Model. If they appear to be randomly scattered then this is a positive sign that the
Model is unbiased.
Saving the plot
You can save the plot using the
filename parameter. The plot is saved in the current working directory unless another path specifed.
If the extension is not specified then it is saved as a png file.
This function can also be found in
from feyn.plots import plot_residuals y_true = train['PRICE'] y_pred = best.predict(train) plot_residuals(y_true, y_pred)