5.實戰之參數調優

基本概念

        在構建模型時，調參（超參數）是極爲重要的一個步驟，因爲只有選擇最佳的參數才能構建一個最優的模型。但是應該如何確定參數的值呢？一般可以通過交叉驗證的方法。什麼是交叉驗證？我K-折交叉驗證爲例。當K=2時，就是我們說的2-折交叉驗證；當K=5時，就是5折-交叉驗證。5-折交叉驗證的原理，如下圖所示。

        如何利用交叉驗證來選擇參數？我們利用循環不斷改變參數，利用交叉驗證法評估每個參數值的模型性能，最終選擇性能最好的參數。

示例演示

        sklearn庫提供了GridSearchCV模塊，用來對模型參數進行調優。grid search採用基於網格搜索的交叉驗證法來選擇模型參數，避免了參數選擇的盲目性和隨意性。這裏給出sklearn的官方示例：
Parameter estimation using grid search with cross-validation。

"""
============================================================
Parameter estimation using grid search with cross-validation
============================================================

This examples shows how a classifier is optimized by cross-validation,
which is done using the :class:`sklearn.model_selection.GridSearchCV` object
on a development set that comprises only half of the available labeled data.

The performance of the selected hyper-parameters and trained model is
then measured on a dedicated evaluation set that was not used during
the model selection step.

More details on tools available for model selection can be found in the
sections on :ref:`cross_validation` and :ref:`grid_search`.

"""
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report
from sklearn.svm import SVC

print(__doc__)

# Loading the Digits dataset
digits = datasets.load_digits()

# To apply an classifier on this data, we need to flatten the image, to
# turn the data in a (samples, feature) matrix:
n_samples = len(digits.images)
X = digits.images.reshape((n_samples, -1))
y = digits.target

# Split the dataset in two equal parts
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.5, random_state=0)

# Set the parameters by cross-validation
tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],
                     'C': [1, 10, 100, 1000]},
                    {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]

scores = ['precision', 'recall']

for score in scores:
    print("# Tuning hyper-parameters for %s" % score)
    print()

    clf = GridSearchCV(
        SVC(), tuned_parameters, scoring='%s_macro' % score
    )
    clf.fit(X_train, y_train)

    print("Best parameters set found on development set:")
    print()
    print(clf.best_params_)
    print()
    print("Grid scores on development set:")
    print()
    means = clf.cv_results_['mean_test_score']
    stds = clf.cv_results_['std_test_score']
    for mean, std, params in zip(means, stds, clf.cv_results_['params']):
        print("%0.3f (+/-%0.03f) for %r"
              % (mean, std * 2, params))
    print()

    print("Detailed classification report:")
    print()
    print("The model is trained on the full development set.")
    print("The scores are computed on the full evaluation set.")
    print()
    y_true, y_pred = y_test, clf.predict(X_test)
    print(classification_report(y_true, y_pred))
    print()

# Note the problem is too easy: the hyperparameter plateau is too flat and the
# output model is the same for precision and recall with ties in quality.

運行結果