X = data.drop('status', axis=1)是什么意思
时间: 2024-04-27 20:24:15 浏览: 12
`X = data.drop('status', axis=1)`这一行代码是将数据集 `data` 中的 `'status'` 列从数据集中删除,并将剩余的列作为特征矩阵 `X`。其中 `axis=1` 的作用是指定删除的是列而不是行。这种操作常用于将原始数据集中的标签或响应变量分离出来,以便于对数据进行预处理和建模。
在这个例子中,原始数据集中的每一行包含一个机器的温度、电流等参数和对应的状态(正常或异常)。通过将 `'status'` 列删除,我们得到一个新的数据集,其中每一行仍然包含机器的参数,但没有对应的状态。这个新的数据集就能够作为模型的特征矩阵 `X`,用于训练和测试模型。
相关问题
data.drop('status', axis=1), data['status'], test_size=0.2)
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.
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。