天猫用户重复购买预测（速通二）

模型训练

分类相关模型

1、逻辑回归分类模型

from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler

stdScaler = StandardScaler()
X = stdScaler.fit_transform(train)

# Split the data into a training set and a test set
X_train, X_test, y_train, y_test = train_test_split(X, target, random_state=0)

clf = LogisticRegression(random_state=0, solver='lbfgs', multi_class='multinomial').fit(X_train, y_train)
clf.score(X_test, y_test)

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2、K近邻分类模型

from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler

stdScaler = StandardScaler()
X = stdScaler.fit_transform(train)

# Split the data into a training set and a test set
X_train, X_test, y_train, y_test = train_test_split(X, target, random_state=0)

clf = KNeighborsClassifier(n_neighbors=3).fit(X_train, y_train)
clf.score(X_test, y_test)

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3、高斯贝叶斯分类模型

4、决策树分类模型

from sklearn import tree

# Split the data into a training set and a test set
X_train, X_test, y_train, y_test = train_test_split(train, target, random_state=0)

clf = tree.DecisionTreeClassifier()
clf = clf.fit(X_train, y_train)
clf.score(X_test, y_test)

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5、集成学习分类模型

集成学习分类模型主要包括Bagging、Boosting、 集成学习投票法、随机森林、LightGBM、极端随机树(ExtraTree, ET) 等常用方法和模型。

Bagging：

from sklearn.ensemble import BaggingClassifier
from sklearn.neighbors import KNeighborsClassifier

# Split the data into a training set and a test set
X_train, X_test, y_train, y_test = train_test_split(train, target, random_state=0)
clf = BaggingClassifier(KNeighborsClassifier(), max_samples=0.5, max_features=0.5)

clf = clf.fit(X_train, y_train)
clf.score(X_test, y_test)

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Boosting：

from sklearn.ensemble import GradientBoostingClassifier

# Split the data into a training set and a test set
X_train, X_test, y_train, y_test = train_test_split(train, target, random_state=0)
clf = GradientBoostingClassifier(n_estimators=10, learning_rate=1.0, max_depth=1, random_state=0)

clf = clf.fit(X_train, y_train)
clf.score(X_test, y_test)

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集成学习投票法：

集成学习投票法的特点:

●绝对多数投票法:某标记超过半数。
●相对多数投票法:预测为得票最多的标记，若同时有多个标记的票最高，则从中随机选取一个。
●加权投票法: 提供了预测结果，与加权平均法类似。

from sklearn import datasets
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import VotingClassifier
from sklearn.preprocessing import StandardScaler

stdScaler = StandardScaler()
X = stdScaler.fit_transform(train)
y = target


clf1 = LogisticRegression(solver='lbfgs', multi_class='multinomial', random_state=1)
clf2 = RandomForestClassifier(n_estimators=50, random_state=1)
clf3 = GaussianNB()

eclf = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2), ('gnb', clf3)], voting='hard')

for clf, label in zip([clf1, clf2, clf3, eclf], ['Logistic Regression', 'Random Forest', 'naive Bayes', 'Ensemble']):
    scores = cross_val_score(clf, X, y, cv=5, scoring='accuracy')
    print("Accuracy: %0.2f (+/- %0.2f) [%s]" % (scores.mean(), scores.std(), label))

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随机森林：

from sklearn.ensemble import RandomForestClassifier

# Split the data into a training set and a test set
X_train, X_test, y_train, y_test = train_test_split(train, target, random_state=0)
clf = clf = RandomForestClassifier(n_estimators=10, max_depth=3, min_samples_split=12, random_state=0)

clf = clf.fit(X_train, y_train)
clf.score(X_test, y_test)

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LightGBM:

LightGBM分类模型和前面介绍过的LightGBM回归模型相似，它可以支持高效率的并行训练，具有更快的训练速度、更低的内存消耗、更好的准确率、分布式支持、可以快速。

import lightgbm

X_train, X_test, y_train, y_test = train_test_split(train, target, test_size=0.4, random_state=0)
X_test, X_valid, y_test, y_valid = train_test_split(X_test, y_test, test_size=0.5, random_state=0)

clf = lightgbm

train_matrix = clf.Dataset(X_train, label=y_train)
test_matrix = clf.Dataset(X_test, label=y_test)
params = {
          'boosting_type': 'gbdt',
          #'boosting_type': 'dart',
          'objective': 'multiclass',
          'metric': 'multi_logloss',
          'min_child_weight': 1.5,
          'num_leaves': 2**5,
          'lambda_l2': 10,
          'subsample': 0.7,
          'colsample_bytree': 0.7,
          'colsample_bylevel': 0.7,
          'learning_rate': 0.03,
          'tree_method': 'exact',
          'seed': 2017,
          "num_class": 2,
          'silent': True,
          }
num_round = 10000
early_stopping_rounds = 100
model = clf.train(params, 
                  train_matrix,
                  num_round,
                  valid_sets=test_matrix,
                  early_stopping_rounds=early_stopping_rounds)
pre= model.predict(X_valid,num_iteration=model.best_iteration)
print('score : ', np.mean((pre[:,1]>0.5)==y_valid))

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极端随机树：

也称ET或Extra-Trees，与随机森林算法非常类似，由许多决策树构成。

极端随机树模型与随机森林模型的主要区别:随机森林应用的是Bagging 模型，极端随机树使用所有的训练样本计算每棵决策树。随机森林是在一个随机子集内得到最佳的分叉属性，而极端随机树模型是依靠完全随机得到分叉值，进而实现对决策树进行分叉。

from sklearn.ensemble import ExtraTreesClassifier

# Split the data into a training set and a test set
X_train, X_test, y_train, y_test = train_test_split(train, target, random_state=0)
clf = ExtraTreesClassifier(n_estimators=10, max_depth=None, min_samples_split=2, random_state=0)

clf = clf.fit(X_train, y_train)
clf.score(X_test, y_test)
clf.n_features_
clf.feature_importances_[:10]
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AdaBoost模型：

from sklearn.ensemble import AdaBoostClassifier

# Split the data into a training set and a test set
X_train, X_test, y_train, y_test = train_test_split(train, target, random_state=0)
clf = AdaBoostClassifier(n_estimators=10)

clf = clf.fit(X_train, y_train)
clf.score(X_test, y_test)
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GDBT模型：

from sklearn.ensemble import GradientBoostingClassifier

# Split the data into a training set and a test set
X_train, X_test, y_train, y_test = train_test_split(train, target, random_state=0)
clf = GradientBoostingClassifier(n_estimators=10, learning_rate=1.0, max_depth=1, random_state=0)

clf = clf.fit(X_train, y_train)
clf.score(X_test, y_test)
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XGB模型：

import xgboost

X_train, X_test, y_train, y_test = train_test_split(train, target, test_size=0.4, random_state=0)
X_test, X_valid, y_test, y_valid = train_test_split(X_test, y_test, test_size=0.5, random_state=0)

clf = xgboost

train_matrix = clf.DMatrix(X_train, label=y_train, missing=-1)
test_matrix = clf.DMatrix(X_test, label=y_test, missing=-1)
z = clf.DMatrix(X_valid, label=y_valid, missing=-1)
params = {'booster': 'gbtree',
          'objective': 'multi:softprob',
          'eval_metric': 'mlogloss',
          'gamma': 1,
          'min_child_weight': 1.5,
          'max_depth': 5,
          'lambda': 100,
          'subsample': 0.7,
          'colsample_bytree': 0.7,
          'colsample_bylevel': 0.7,
          'eta': 0.03,
          'tree_method': 'exact',
          'seed': 2017,
          "num_class": 2
          }

num_round = 10000
early_stopping_rounds = 100
watchlist = [(train_matrix, 'train'),
             (test_matrix, 'eval')
             ]

model = clf.train(params,
                  train_matrix,
                  num_boost_round=num_round,
                  evals=watchlist,
                  early_stopping_rounds=early_stopping_rounds
                  )
pre = model.predict(z,ntree_limit=model.best_ntree_limit)
print('score : ', np.mean((pre[:,1]>0.3)==y_valid))

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模型验证

模型验证指标

特征优化

特征选择技巧

1、搜索算法

当特征数量较多时，常用如下方法:

穷举法(Exhaustive): 暴力穷尽。
贪心法(Greedy Selection);线性时间。
模拟退火( Simulated Annealing):随机尝试找最优。
基因算法(Genetic Algorithm):组合深度优先尝试。
邻居搜索(Variable Neighbor Search);利用相近关系搜索。

2、特征选择方法

特征选择的方法主要有过滤法(Filter)、 包装法(Wrapper) 和嵌入法( Embedded )。