Xgboost 两种使用方式

原生形式使用Xgboost(import xgboost as xgb)

from sklearn import datasets

from sklearn.model_selection import train_test_split

import xgboost as xgb

import numpy as np

from sklearn.metrics import precision_score, recall_score

# 加载数据

iris = datasets.load_iris()

X = iris.data

y = iris.target

# 划分训练集和测试集

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)

print("Train data length:", len(X_train))

print("Test data length:", len(X_test))

# 转换为DMatrix数据格式

dtrain = xgb.DMatrix(X_train, label=y_train)

dtest = xgb.DMatrix(X_test, label=y_test)

# 设置参数

parameters = {

    'eta': 0.3,

    'silent': True,  # option for logging

    'objective': 'multi:softprob',  # error evaluation for multiclass tasks

    'num_class': 3,  # number of classes to predic

    'max_depth': 3  # depth of the trees in the boosting process

}

num_round = 20  # the number of training iterations

# 模型训练

bst = xgb.train(parameters, dtrain, num_round)

# 模型预测

preds = bst.predict(dtest)

print(preds[:5])

# 选择表示最高概率的列

best_preds = np.asarray([np.argmax(line) for line in preds])

print(best_preds)

# 模型评估

print(precision_score(y_test, best_preds, average='macro'))  # 精准率

print(recall_score(y_test, best_preds, average='macro'))  # 召回率

Sklearn接口形式使用Xgboost(from xgboost import XGBClassifier)

from sklearn import datasets

from sklearn.model_selection import train_test_split

from xgboost import XGBClassifier

from sklearn.metrics import precision_score, recall_score

# 加载数据

iris = datasets.load_iris()

X = iris.data

y = iris.target

# 划分训练集和测试集

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)

print("Train data length:", len(X_train))

print("Test data length:", len(X_test))

# 模型训练

model = XGBClassifier(

        learning_rate=0.01,

        n_estimators=3000,

        max_depth=4,

        min_child_weight=5,

        gamma=0.1,

        subsample=0.8,

        colsample_bytree=0.8,

        reg_alpha=1,

        objective='binary:logistic',

        nthread=8,

        scale_pos_weight=1,

        seed=27

    )

model.fit(X_train, y_train)

# 预测

y_pred = model.predict(X_test)

# 模型评估

print(precision_score(y_test, y_pred, average='macro'))  # 精准率

print(recall_score(y_test, y_pred, average='macro'))  # 召回率

巴特西

Xgboost 两种使用方式

原生形式使用Xgboost(import xgboost as xgb)

Sklearn接口形式使用Xgboost(from xgboost import XGBClassifier)

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