这是一个演示，展示了如何结合特征以及如何使用调整超参数GridSearchCV。

不幸的是，您的样本数据集太小而无法训练真实模型。

try:
    from pathlib import Path
except ImportError:             # Python 2
    from pathlib2 import Path
import os
import re
from pprint import pprint
import pandas as pd
import numpy as np
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.preprocessing import FunctionTransformer, LabelEncoder, LabelBinarizer, StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.feature_selection import SelectPercentile
from sklearn.feature_extraction import DictVectorizer
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import SGDClassifier
from sklearn.naive_bayes import MultinomialNB, GaussianNB
from sklearn.neural_network import MLPClassifier
from sklearn.svm import SVC
from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.externals import joblib
from scipy.sparse import csr_matrix, hstack


class ColumnSelector(BaseEstimator, TransformerMixin):

    def __init__(self, name=None, position=None,
                 as_cat_codes=False, sparse=False):
        self.name = name
        self.position = position
        self.as_cat_codes = as_cat_codes
        self.sparse = sparse

    def fit(self, X, y=None):
        return self

    def transform(self, X, **kwargs):
        if self.name is not None:
            col_pos = X.columns.get_loc(self.name)
        elif self.position is not None:
            col_pos = self.position
        else:
            raise Exception('either [name] or [position] parameter must be not-None')
        if self.as_cat_codes and X.dtypes.iloc[col_pos] == 'category':
                ret = X.iloc[:, col_pos].cat.codes
        else:
            ret = X.iloc[:, col_pos]
        if self.sparse:
            ret = csr_matrix(ret.values.reshape(-1,1))
        return ret

union = FeatureUnion([
            ('text', 
             Pipeline([
                ('select', ColumnSelector('url')),
                #('pct', SelectPercentile(percentile=1)),
                ('vect', TfidfVectorizer(sublinear_tf=True, max_df=0.5,
                                         stop_words='english')),
             ]) ),
            ('ads',
             Pipeline([
                ('select', ColumnSelector('ads_keyword', sparse=True,
                                          as_cat_codes=True)),
                #('scale', StandardScaler(with_mean=False)),
             ]) )
        ])

pipe = Pipeline([
    ('union', union),
    ('clf', MultinomialNB())
])

param_grid = [
    {
        'union__text__vect': [TfidfVectorizer(sublinear_tf=True,
                                              max_df=0.5,
                                              stop_words='english')],
        'clf': [SGDClassifier(max_iter=500)],
        'union__text__vect__ngram_range': [(1,1), (2,5)],
        'union__text__vect__analyzer': ['word','char_wb'],
        'clf__alpha': np.logspace(-5, 0, 6),
        #'clf__max_iter': [500],
    },
    {
        'union__text__vect': [TfidfVectorizer(sublinear_tf=True,
                                              max_df=0.5,
                                              stop_words='english')],
        'clf': [MultinomialNB()],
        'union__text__vect__ngram_range': [(1,1), (2,5)],
        'union__text__vect__analyzer': ['word','char_wb'],
        'clf__alpha': np.logspace(-4, 2, 7),
    },
    #{        # NOTE: does NOT support sparse matrices!
    #    'union__text__vect': [TfidfVectorizer(sublinear_tf=True,
    #                                          max_df=0.5,
    #                                          stop_words='english')],
    #    'clf': [GaussianNB()],
    #    'union__text__vect__ngram_range': [(1,1), (2,5)],
    #    'union__text__vect__analyzer': ['word','char_wb'],
    #},
]

gs_kwargs = dict(scoring='roc_auc', cv=3, n_jobs=1, verbose=2)
X_train, X_test, y_train, y_test = \
    train_test_split(df[['url','ads_keyword']], df['target'], test_size=0.33)
grid = GridSearchCV(pipe, param_grid=param_grid, **gs_kwargs)
grid.fit(X_train, y_train)

# prediction
predicted = grid.predict(X_test)