by: Chris Cave
(Feyn version 1.5.4 or newer)
Feyn offers a range of tools to help you dissect your graph and its dynamics.
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 I import data, prepare it and find my graph of choice with my QLattice:
from sklearn.datasets import load_boston import pandas as pd from feyn import QLattice 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) # Connect to QLattice ql = QLattice() ql.reset() # Get a regressor qgraph = ql.get_regressor(train, 'PRICE', max_depth = 2) #max_depth = 2, let's not overdo it qgraph.fit(train) # Select a graph from your fitted QGraph best_graph = qgraph
Goodness of fit
As I have a regressor, I would like to compare the true values of my target variable with my predicted values. The code below plots tuples: on the x-axis is the true values of the target variable and on the y-axis is the predicted values. If the prediction is perfect then all the points should lie on the
y=x dashed line. I can use this to see whether I overestimate or underestimate certain regions.
The line of best fit is an aid to see just how close the points are to the line of equality.