code for test and train split
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.33, random_state=42)code for test and train split
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.33, random_state=42)sklearn split train test
import numpy as np
from sklearn.model_selection import train_test_split
X, y = np.arange(10).reshape((5, 2)), range(5)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.33, random_state=42)
X_train
# array([[4, 5],
# [0, 1],
# [6, 7]])
y_train
# [2, 0, 3]
X_test
# array([[2, 3],
# [8, 9]])
y_test
# [1, 4]split data train, test by id python
train_inds, test_inds = next(GroupShuffleSplit(test_size=.20, n_splits=2, random_state = 7).split(df, groups=df['Group_Id']))
train = df.iloc[train_inds]
test = df.iloc[test_inds]how to distribute a dataset in train and test using scikit
from sklearn.model_selection import train_test_split
xTrain, xTest, yTrain, yTest = train_test_split(x, y, test_size = 0.2, random_state = 0)train test split sklearn
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=42)
print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)code for test and train split
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y,
test_size = 0.33, random_state = 42)Copyright © 2021 Codeinu
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