by: Chris Cave
(Feyn version 2.0.0 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) # Connect to QLattice ql = feyn.connect_qlattice() models = ql.auto_run( data=train, output_name='PRICE' ) # Select the best Model best = models
As we have a regressor, we would like to compare the true values of the target variable with the predicted values. The code below plots tuples: on the x-axis are the true values of the target variable and on the y-axis are the predicted values from the regressor. If the prediction is perfect then all the points should lie on the
y=x dashed line. We can use this to see whether we 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.
This function can also be found in
from feyn.plots import plot_regression y_true = train['PRICE'] y_pred = best.predict(train) plot_regression(y_true, y_pred)