存储xgboostmodel

最新推荐文章于 2025-04-08 14:30:12 发布

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最新推荐文章于 2025-04-08 14:30:12 发布

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本文介绍如何使用Python的XGBoost库训练糖尿病预测模型，并利用pickle模块将模型保存到文件中，以便后续加载和进行预测。文章详细展示了数据预处理、模型训练、保存与加载的过程，以及如何评估模型的准确性。

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# Train XGBoost model, save to file using pickle, load and make predictions
from numpy import loadtxt
import xgboost
import pickle
from sklearn import model_selection
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 = cross_validation.train_test_split(X, Y, test_size=test_size, random_state=seed)
# fit model no training data
model = xgboost.XGBClassifier()
model.fit(X_train, y_train)
# save model to file
pickle.dump(model, open("pima.pickle.dat", "wb"))

# some time later...

# load model from file
loaded_model = pickle.load(open("pima.pickle.dat", "rb"))
# make predictions for test data
y_pred = loaded_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))

https://machinelearningmastery.com/save-gradient-boosting-models-xgboost-python/