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Train-Dev-Test Split (Training-Validation-Testing Split):
Ratio for Splitting Dataset into Training, Validation and Test Sets
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The train-dev-test split, also known as the training-validation-testing split, is a common practice in machine learning. The typical ratio for splitting your dataset into training, validation, and test sets is commonly referred to as the "70-30" or "80-20" rule. However, the exact ratios can vary depending on the size of your dataset and the specific problem you're working on:

  1. Training Set: This portion of the dataset is the largest portion of the dataset and is used to train your machine learning model. It typically accounts for the largest portion of the data, ranging from 60% to 80% of the total dataset. In some cases, even up to 90% of the data may be used for training if the dataset is very large.

  2. Validation Set: The validation set is used to fine-tune the model's hyperparameters and assess its performance during training. It helps in preventing overfitting, as the model's performance on the validation set can guide adjustments to the model's parameters. The validation set is used during the training process to tune hyperparameters, assess model performance, and prevent overfitting. It typically represents about 10% to 20% of the total dataset. The validation set helps you make decisions about your model's architecture, regularization, and other settings.

  3. Test Set: The test set is used to evaluate the final performance of your trained machine learning model. This set is not used during the training or parameter tuning process and serves as an independent benchmark to assess how well the model generalizes to unseen data. That is, it should be kept separate from the training and validation data and is used to provide an unbiased estimate of how well your model will generalize to unseen data. The test set typically represents the remaining 10% to 20% of the data.

A few additional considerations are:

  • Cross-Validation: In some cases, especially when you have limited data, you might use techniques like k-fold cross-validation. In k-fold cross-validation, the data is divided into k subsets (folds), and the training/validation process is repeated k times, with each fold serving as the validation set once. This helps to make efficient use of available data for both training and validation.

  • Imbalanced Datasets: If your dataset is highly imbalanced (i.e., one class has significantly more samples than others), you may need to adjust the ratios to ensure that each set (training, validation, test) has a representative distribution of classes.

  • Stratified Sampling: In classification problems, it's often a good practice to use stratified sampling to ensure that each class is represented proportionally in all three sets. This helps prevent situations where one of the sets has very few examples of a particular class.

The specific ratios and strategies you choose can depend on the nature of your data, the problem you're trying to solve, and the computational resources available. Experimentation and validation using appropriate evaluation metrics are essential to determine the best split ratios for your particular machine learning project.

Google Cloud does not prescribe specific ratios for splitting your dataset into training, validation, and test sets. The choice of dataset split ratios is typically left to the discretion of the data scientist or machine learning engineer working on the project. Google Cloud provides the infrastructure and tools for machine learning, but it does not dictate the specifics of how you should structure your datasets.

In Python, the scikit-learn library provides the train_test_split function that can be used to easily split a dataset into training and testing sets:

          from sklearn.model_selection import train_test_split

          # Assuming X is your feature matrix and y is your target variable
          X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

This code snippet splits the dataset into 80% training data (X_train and y_train) and 20% testing data (X_test and y_test). The random_state parameter ensures reproducibility by fixing the random seed.