block13.python

# permutation feature importance with knn for regression
from sklearn.neighbors import KNeighborsRegressor
from sklearn.inspection import permutation_importance
from matplotlib import pyplot as plt 
# define the model
model = KNeighborsRegressor()
# fit the model
model.fit(X_reg, y_reg)
# perform permutation importance
results = permutation_importance(model, X_reg, y_reg, scoring='neg_mean_squared_error')
# get importance
importance = results.importances_mean
# summarize feature importance
for i,v in enumerate(importance):
	print('Feature: %0d, Score: %.5f' % (i,v))
# plot feature importance
plt.bar([x for x in range(len(importance))], importance)
plt.show()