我在一个文本列上运行tf-idf，并将其用于逻辑回归。这是逻辑回归中我使用的唯一列。我如何确保这些参数被尽可能好地调整？

我希望能够通过一系列步骤，最终能够说我的逻辑回归分类器运行得尽可能好。

from sklearn import metrics,preprocessing,cross_validationfrom sklearn.feature_extraction.text import TfidfVectorizerimport sklearn.linear_model as lmimport pandas as ploadData = lambda f: np.genfromtxt(open(f, 'r'), delimiter=' ')print "loading data.."traindata = list(np.array(p.read_table('train.tsv'))[:, 2])testdata = list(np.array(p.read_table('test.tsv'))[:, 2])y = np.array(p.read_table('train.tsv'))[:, -1]tfv = TfidfVectorizer(min_df=3, max_features=None, strip_accents='unicode',                      analyzer='word', token_pattern=r'\w{1,}',                       ngram_range=(1, 2), use_idf=1, smooth_idf=1,                       sublinear_tf=1)rd = lm.LogisticRegression(penalty='l2', dual=True, tol=0.0001,                            C=1, fit_intercept=True, intercept_scaling=1.0,                            class_weight=None, random_state=None)X_all = traindata + testdatalentrain = len(traindata)print "fitting pipeline"tfv.fit(X_all)print "transforming data"X_all = tfv.transform(X_all)X = X_all[:lentrain]X_test = X_all[lentrain:]print "20 Fold CV Score: ", np.mean(cross_validation.cross_val_score(rd, X, y, cv=20, scoring='roc_auc'))print "training on full data"rd.fit(X, y)pred = rd.predict_proba(X_test)[:, 1]testfile = p.read_csv('test.tsv', sep="\t", na_values=['?'], index_col=1)pred_df = p.DataFrame(pred, index=testfile.index, columns=['label'])pred_df.to_csv('benchmark.csv')print "submission file created.."

回答：

你可以使用网格搜索来找到最佳的C值。基本上，较小的C值指定了更强的正则化。

>>> param_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000] }>>> clf = GridSearchCV(LogisticRegression(penalty='l2'), param_grid)GridSearchCV(cv=None,             estimator=LogisticRegression(C=1.0, intercept_scaling=1,                  dual=False, fit_intercept=True, penalty='l2', tol=0.0001),             param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]})

有关在你的应用中更多详细信息，请查看GridSearchCv文档。