【机器学习】--xgboost初始之代码实现分类
【机器学习】--xgboost初始之代码实现分类
LhWorld哥陪你聊算法
发布于 2018-09-13 15:16:59
发布于 2018-09-13 15:16:59
文章被收录于专栏：LhWorld哥陪你聊算法
一、前述
上节我们讲解了xgboost的基本知识，本节我们通过实例进一步讲解。
二、具体
1、安装
默认可以通过pip安装，若是安装不上可以通过https://www.lfd.uci.edu/~gohlke/pythonlibs/网站下载相关安装包,将安装包拷贝到Anacoda3的安装目录的Scrripts目录下， 然后pip install 安装包安装。
2、代码实例
import xgboost
# First XGBoost model for Pima Indians dataset
from numpy import loadtxt
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# load data
dataset = loadtxt('pima-indians-diabetes.csv', delimiter=",")
# split data into X and y
X = dataset[:,0:8]
Y = dataset[:,8]
# split data into train and test sets
seed = 7
test_size = 0.33
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=test_size, random_state=seed)
# fit model no training data
model = XGBClassifier()
model.fit(X_train, y_train)
# make predictions for test data
y_pred = model.predict(X_test)
predictions = [round(value) for value in y_pred]
# evaluate predictions
accuracy = accuracy_score(y_test, predictions)
print("Accuracy: %.2f%%" % (accuracy * 100.0))或者每次插入一颗树，看看效果
from numpy import loadtxt
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# load data
dataset = loadtxt('pima-indians-diabetes.csv', delimiter=",")
# split data into X and y
X = dataset[:,0:8]
Y = dataset[:,8]
# split data into train and test sets
seed = 7
test_size = 0.33
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=test_size, random_state=seed)
# fit model no training data
model = XGBClassifier()
eval_set = [(X_test, y_test)]
model.fit(X_train, y_train, early_stopping_rounds=10, eval_metric="logloss", eval_set=eval_set, verbose=True)
# make predictions for test data
y_pred = model.predict(X_test)
predictions = [round(value) for value in y_pred]
# evaluate predictions
accuracy = accuracy_score(y_test, predictions)
print("Accuracy: %.2f%%" % (accuracy * 100.0))观看特征的重要程度：
from numpy import loadtxt
from xgboost import XGBClassifier
from xgboost import plot_importance
from matplotlib import pyplot
# load data
dataset = loadtxt('pima-indians-diabetes.csv', delimiter=",")
# split data into X and y
X = dataset[:,0:8]
y = dataset[:,8]
# fit model no training data
model = XGBClassifier()
model.fit(X, y)
# plot feature importance
plot_importance(model)
pyplot.show()
xgboost参数：
- 'booster':'gbtree',
- 'objective': 'multi:softmax', 多分类的问题
- 'num_class':10, 类别数，与 multisoftmax 并用
- 'gamma':损失下降多少才进行分裂
- 'max_depth':12, 构建树的深度，越大越容易过拟合
- 'lambda':2, 控制模型复杂度的权重值的L2正则化项参数，参数越大，模型越不容易过拟合。
- 'subsample':0.7, 随机采样训练样本
- 'colsample_bytree':0.7, 生成树时进行的列采样
- 'min_child_weight':3, 孩子节点中最小的样本权重和。如果一个叶子节点的样本权重和小于min_child_weight则拆分过程结束
- 'silent':0 ,设置成1则没有运行信息输出，最好是设置为0.
- 'eta': 0.007, 如同学习率
- 'seed':1000,
- 'nthread':7, cpu 线程数
xgb1 = XGBClassifier(
learning_rate =0.1,
n_estimators=1000,
max_depth=5,
min_child_weight=1,
gamma=0,
subsample=0.8,
colsample_bytree=0.8,
objective= 'binary:logistic',
nthread=4,
scale_pos_weight=1,
seed=27)交叉验证：
# Tune learning_rate
from numpy import loadtxt
from xgboost import XGBClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import StratifiedKFold
# load data
dataset = loadtxt('pima-indians-diabetes.csv', delimiter=",")
# split data into X and y
X = dataset[:,0:8]
Y = dataset[:,8]
# grid search
model = XGBClassifier()
learning_rate = [0.0001, 0.001, 0.01, 0.1, 0.2, 0.3]
param_grid = dict(learning_rate=learning_rate)
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=7)
grid_search = GridSearchCV(model, param_grid, scoring="neg_log_loss", n_jobs=-1, cv=kfold)
grid_result = grid_search.fit(X, Y)
# summarize results
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
means = grid_result.cv_results_['mean_test_score']
params = grid_result.cv_results_['params']
for mean, param in zip(means, params):
print("%f with: %r" % (mean, param))本文参与 腾讯云自媒体同步曝光计划，分享自作者个人站点/博客。
原始发表：2018-06-18 ，如有侵权请联系 cloudcommunity@tencent.com 删除
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