X_train, X_test, y_train, y_test = train_test_split(independent_variables, target, test_size=0.2, random_state=0)
时间: 2023-10-26 14:43:31 浏览: 37
这是一个用于机器学习的代码示例,其中 independent_variables 是自变量的数据集,target 是因变量的数据集。train_test_split 函数将数据集划分为训练集和测试集,其中训练集占 80%,测试集占 20%。random_state 参数用于设置随机种子,以确保划分结果的可重复性。划分后的数据集分别存储在 X_train、X_test、y_train、y_test 四个变量中。
相关问题
estimator.fit(x_train, y_train)
This line of code is using the "fit" method of the "estimator" object to train (or fit) a machine learning model on the training data, "x_train" and "y_train". The "x_train" variable holds the input features (independent variables) of the training data, and "y_train" holds the corresponding target values (dependent variable). The "fit" method adjusts the parameters of the machine learning model so that it can make accurate predictions on new, unseen data.
(X_train,Y_train),(X_test,Y_test) = mnist.load_data()
This code uses the `load_data()` function from the `mnist` module to load the MNIST dataset. The dataset is split into training and testing sets, with the training set stored in `X_train` and `Y_train` variables, and the testing set stored in `X_test` and `Y_test` variables. `X_train` and `X_test` contain the images of handwritten digits, while `Y_train` and `Y_test` contain the corresponding labels for those images.
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