मैं यह जानने की कोशिश कर रहा हूं कि क्या नेस्टेड क्रॉस-वैलिडेशन की मेरी समझ सही है, इसलिए मैंने यह देखने के लिए यह खिलौना उदाहरण लिखा है कि क्या मैं सही हूं:
import operator
import numpy as np
from sklearn import cross_validation
from sklearn import ensemble
from sklearn.datasets import load_boston
# set random state
state = 1
# load boston dataset
boston = load_boston()
X = boston.data
y = boston.target
outer_scores = []
# outer cross-validation
outer = cross_validation.KFold(len(y), n_folds=3, shuffle=True, random_state=state)
for fold, (train_index_outer, test_index_outer) in enumerate(outer):
X_train_outer, X_test_outer = X[train_index_outer], X[test_index_outer]
y_train_outer, y_test_outer = y[train_index_outer], y[test_index_outer]
inner_mean_scores = []
# define explored parameter space.
# procedure below should be equal to GridSearchCV
tuned_parameter = [1000, 1100, 1200]
for param in tuned_parameter:
inner_scores = []
# inner cross-validation
inner = cross_validation.KFold(len(X_train_outer), n_folds=3, shuffle=True, random_state=state)
for train_index_inner, test_index_inner in inner:
# split the training data of outer CV
X_train_inner, X_test_inner = X_train_outer[train_index_inner], X_train_outer[test_index_inner]
y_train_inner, y_test_inner = y_train_outer[train_index_inner], y_train_outer[test_index_inner]
# fit extremely randomized trees regressor to training data of inner CV
clf = ensemble.ExtraTreesRegressor(param, n_jobs=-1, random_state=1)
clf.fit(X_train_inner, y_train_inner)
inner_scores.append(clf.score(X_test_inner, y_test_inner))
# calculate mean score for inner folds
inner_mean_scores.append(np.mean(inner_scores))
# get maximum score index
index, value = max(enumerate(inner_mean_scores), key=operator.itemgetter(1))
print 'Best parameter of %i fold: %i' % (fold + 1, tuned_parameter[index])
# fit the selected model to the training set of outer CV
# for prediction error estimation
clf2 = ensemble.ExtraTreesRegressor(tuned_parameter[index], n_jobs=-1, random_state=1)
clf2.fit(X_train_outer, y_train_outer)
outer_scores.append(clf2.score(X_test_outer, y_test_outer))
# show the prediction error estimate produced by nested CV
print 'Unbiased prediction error: %.4f' % (np.mean(outer_scores))
# finally, fit the selected model to the whole dataset
clf3 = ensemble.ExtraTreesRegressor(tuned_parameter[index], n_jobs=-1, random_state=1)
clf3.fit(X, y)
किसी भी विचार की सराहना की।
3
क्या आप उन लोगों के लिए पाठ में क्रॉस-मान्यता की अपनी समझ का एक संस्करण प्रदान कर सकते हैं जो पायथन नहीं पढ़ते हैं?
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गूँग - मोनिका
scikit-learnस्वयं का संस्करण: scikit-learn.org/stable/auto_examples/model_selection/…