Shuffling is a common technique used in data preprocessing to randomize the order of data points in a dataset. This technique can be useful in a variety of scenarios, such as training machine learning models, creating validation sets, and preventing bias in the data. Scikit-learn is a popular Python library for machine learning that provides a simple and efficient way to shuffle data. In this thread, we will discuss variety of techniques to shuffle dataset.
1. Using "ShuffleSplit":
The ShuffleSplit method randomly shuffles the data and splits it into two sets, one for training and one for testing.
- The
ShuffleSplitmethod has two important parameters:n_splitsandtest_size.
Here’s an example given below:
In this above example, We loaded the diabetes dataset, split it into features and target. Then, we created a ShuffleSplit object with 5 splits and a test size of 0.3. Next, we looped through each split, split the data into training and testing sets, fitted a linear regression model to the training data, and computed the model’s score on the testing data. Finally, we displayed the score for each split.
2. Using "KFold":
The `KFold` method in Scikit-learn is used for performing k-fold cross-validation on a dataset. In k-fold cross-validation, the dataset is divided into k subsets or "folds".- By default, the
KFoldmethod in Scikit-learn does not shuffle the data. However, you can shuffle the data by setting theshuffleparameter toTrue. Here’s an example:
3. Using "GroupKFold":
The GroupKFold method in Scikit-learn is used for performing k-fold cross-validation on a dataset while preserving the grouping of samples.
- For example, if your data contains multiple samples from the same subject or group, you may want to ensure that all samples from the same subject are in the same fold.