Python 常用库及其作用

最新推荐文章于 2025-09-19 15:02:58 发布

原创最新推荐文章于 2025-09-19 15:02:58 发布 · 2.2k 阅读

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本文介绍了一个使用Python的机器学习流水线构建过程，涵盖了数据预处理、特征选择、模型训练及评估等多个步骤。通过Sklearn库实现了从数据清洗到模型训练的一整套流程，并利用XGBoost等算法进行预测。

import numpy as np
import pandas as pd
import warnings
#import itertools
import time
import numpy as np
import pandas as pd
from time import strftime
import numpy.random as random
from numpy import hstack, vstack
from sklearn.preprocessing import Imputer
from sklearn.preprocessing import RobustScaler
from sklearn.feature_selection import SelectFromModel
from sklearn.feature_selection import VarianceThreshold 
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
import xgboost as xgb
from sklearn.pipeline import Pipeline
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import classification_report
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import Pipeline
from sklearn.feature_selection import f_regression
from sklearn.preprocessing import FunctionTransformer
import xgboost as xgb
from sklearn.feature_selection import SelectFromModel
from sklearn.metrics import classification_report
from sklearn.metrics import roc_curve
from sklearn.metrics import auc
from sklearn.externals import joblib
from sklearn.model_selection import cross_val_score