Python（Sklearn）train_test_split：选择要训练的数据以及要测试的数据
我想使用Sklearn的Train_test_split手动将数据分为火车和测试类别。具体来说，在我的.CSV文件中，我想使用所有数据行，直到最后一行进行训练，然后进行最后一行进行测试。
The reason I'm doing this is because I need to launch a machine learning model but am incredibly short on time. I thought the best way would be to use predictions rather than deploying it using IBM Watson. I don't need it to be live.My code so far looks like this:df=pd.read_csv('Book5.csv', names=['Amiability', 'Email'])
from sklearn.model_selection import train_test_split
df_x = df['Amiability']
df_y = df['Email']
x_train, x_test, y_train, y_test = train_test_split(df_x, df_y, test_size=0.2, random_state=4)
然后，
len(df)
生产
331
我要用0-330行训练，然后用第331行进行测试。我该怎么做？
I want to use sklearn's train_test_split to manually split data into train and test categories. Specifically, in my .csv file, I want to use all the rows of data until the last row to train, and the last row to test.
The reason I'm doing this is because I need to launch a machine learning model but am incredibly short on time. I thought the best way would be to use predictions rather than deploying it using IBM Watson. I don't need it to be live.
My code so far looks like this:
df=pd.read_csv('Book5.csv', names=['Amiability', 'Email'])
from sklearn.model_selection import train_test_split
df_x = df['Amiability']
df_y = df['Email']
x_train, x_test, y_train, y_test = train_test_split(df_x, df_y, test_size=0.2, random_state=4)
Then,
len(df)
Produces
331
I want to train with rows 0-330, and test with row 331. How can I do this?
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如果您绝对不需要测试行是最后一行，则应该能够做到：
当
test_size =是一个整数时，它指定了测试集的绝对样本行数。If you don't absolutely need the test row to be the last row you should be able to do:
When
test_size=is an integer it specifies the absolute number of sample rows for the test set.