data.drop('status', axis=1), data['status'], test_size=0.2)
时间: 2024-01-27 07:06:25 浏览: 25
This code is likely part of a machine learning workflow and is used to split a dataset into training and testing sets.
The first part, `data.drop('status', axis=1)`, drops the column named 'status' from the dataset. The `axis=1` parameter indicates that this column should be dropped from the columns axis (i.e. horizontally).
The second part, `data['status']`, selects the 'status' column from the dataset.
The final part, `test_size=0.2`, specifies that the testing set should comprise 20% of the dataset, while the remaining 80% will be used for training. The `train_test_split()` function is commonly used to randomly split data into training and testing sets for machine learning models.
相关问题
import pandas as pd from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.preprocessing import OneHotEncoder,LabelEncoder from sklearn.model_selection import cross_val_score from sklearn.model_selection import GridSearchCV df = pd.read_csv('mafs(1).csv') df.head() man = df['Gender']=='M' woman = df['Gender']=='F' data = pd.DataFrame() data['couple'] = df.Couple.unique() data['location'] = df.Location.values[::2] data['man_name'] = df.Name[man].values data['woman_name'] = df.Name[woman].values data['man_occupation'] = df.Occupation[man].values data['woman_occupaiton'] = df.Occupation[woman].values data['man_age'] = df.Age[man].values data['woman_age'] = df.Age[woman].values data['man_decision'] = df.Decision[man].values data['woman_decision']=df.Decision[woman].values data['status'] = df.Status.values[::2] data.head() data.to_csv('./data.csv') data = pd.read_csv('./data.csv',index_col=0) data.head() enc = OneHotEncoder() matrix = enc.fit_transform(data['location'].values.reshape(-1,1)).toarray() feature_labels = enc.categories_ loc = pd.DataFrame(data=matrix,columns=feature_labels) data_new=data[['man_age','woman_age','man_decision','woman_decision','status']] data_new.head() lec=LabelEncoder() for label in ['man_decision','woman_decision','status']: data_new[label] = lec.fit_transform(data_new[label]) data_final = pd.concat([loc,data_new],axis=1) data_final.head() X = data_final.drop(columns=['status']) Y = data_final.status X_train,X_test,Y_train,Y_test=train_test_split(X,Y,train_size=0.7,shuffle=True) rfc = RandomForestClassifier(n_estimators=20,max_depth=2) param_grid = [ {'n_estimators': [3, 10, 30,60,100], 'max_features': [2, 4, 6, 8], 'max_depth':[2,4,6,8,10]}, ] grid_search = GridSearchCV(rfc, param_grid, cv=9) grid_search.fit(X, Y) print(grid_search.best_score_) #最好的参数 print(grid_search.best_params_)
这段代码是使用随机森林分类器对一个约会节目的参赛者进行分类的,根据他们的年龄、职业、决策等信息,将他们的状态(是否找到约会对象)进行预测。代码中使用了OneHotEncoder和LabelEncoder对分类变量进行编码,使用GridSearchCV对超参数进行调优。最后输出了最好的参数和相应的得分。
import numpy as np import pandas as pd train_data = pd.read_csv("C://Users//Dell//Desktop//数据分析作业//adult_train(1).csv") test_data = pd.read_csv("C://Users//Dell//Desktop//数据分析作业//adult_test.csv") #写入csv文件 columns = ['Age','Workclass','fnlgwt','Education','EdNum','MaritalStatus', 'Occupation','Relationship','Race','Sex','CapitalGain', 'CapitalLoss','HoursPerWeek','Country','Income'] #写入名称 df_train_set = pd.read_csv('C://Users//Dell//Desktop//数据分析作业//adult_train(1).csv', names=columns) #跳过表头 df_test_set = pd.read_csv('C://Users//Dell//Desktop//数据分析作业//adult_test.csv', names=columns, skiprows=1) #删除ID序列号栏 df_train_set.drop('fnlgwt', axis=1, inplace=True) df_test_set.drop('fnlgwt', axis=1, inplace=True) #用unknown替换? for i in df_train_set.columns: df_train_set[i].replace('?', 'Unknown', inplace=True) df_test_set[i].replace('?', 'Unknown', inplace=True) #去掉非int64类型数据中的点和空格 for col in df_train_set.columns: if df_train_set[col].dtype != 'int64': df_train_set[col] = df_train_set[col].apply(lambda val: val.replace(" ", "")) df_train_set[col] = df_train_set[col].apply(lambda val: val.replace(".", "")) df_test_set[col] = df_test_set[col].apply(lambda val: val.replace(" ", "")) df_test_set[col] = df_test_set[col].apply(lambda val: val.replace(".", "")) # 将训练集导出为 csv 文件 df_train_set.to_csv('train_set.csv', index=False) # 将测试集导出为 csv 文件 df_test_set.to_csv('test_set.csv', index=False)
这段代码是用来读取和预处理成人收入数据集(Adult dataset)的。其中包括读取训练集和测试集的csv文件,去掉ID序列号栏,用"Unknown"替换缺失值"?",去除非int64类型数据中的点和空格,并将处理后的数据集导出为csv文件。该数据集是一个二分类问题,目标是预测一个人的收入是否超过50K美元。这段代码中的预处理是为了对数据进行清洗和转换,以便更好地进行建模和预测。