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Splitting a dataset is a common practice in machine learning to avoid overfitting the model to the training data and to evaluate the models generalization performance on unseen data. Typically, a random split is made with a fixed ratio (e.g., 80% for training and 20% for testing) or a predefined number of folds (k) for cross-validation. ...

The most common type of splitter is the train-test splitter, which divides the dataset into two subsets: the training set and the test set. The training set is used to train the machine learning model, while the test set is used to evaluate the performance of the trained model. Typically, the training set is ...

In traditional machine learning approaches, separate models are trained for each task, which can be computationally expensive and lead to overfitting due to the limited amount of data available for each task. ...

Unlike model parameters, hyperparameters are not learned during the training process but rather are set by the machine learning engineer or data scientist. Hyperparameters control the ...

This process typically includes several steps, which may vary depending on the specific task and the nature of the data: **Text Preprocessing**: This step involves cleaning the text data to remove any unwanted characters, symbols, or numbers. This can include tasks like removing punctuation, converting all ...