logisticregression python参数设置_Logistic回归中的参数微调

我正在运行一个逻辑回归，一个tf-idf正在一个文本列上运行。这是我在逻辑回归中使用的唯一一个列。我怎样才能确保这方面的参数尽可能地得到优化？

我希望能够通过一系列的步骤，这将最终允许我说，我的逻辑回归分类器运行以及它可能。from sklearn import metrics,preprocessing,cross_validation

from sklearn.feature_extraction.text import TfidfVectorizer

import sklearn.linear_model as lm

import pandas as p

loadData = 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 + testdata

lentrain = 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.."