Cross Validation

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Cross-validation is a technique used in machine learning to assess the performance and generalization ability of a model. It involves partitioning the available dataset into multiple subsets or folds, with each fold used as both a training set and a validation set.

Here's how cross-validation typically works:

  1. Split the data: The original dataset is divided into k equally-sized folds (or subsets), usually through a process called k-fold cross-validation. Common choices for k are 5 or 10, but it can vary depending on the size of the dataset.

  2. Training and validation: The model is trained on k-1 folds (training set) and validated on the remaining fold (validation set). This process is repeated k times, with each fold used as the validation set once.

  3. Performance evaluation: The performance metric (e.g., accuracy, precision, recall) is calculated for each validation set. The average performance across all folds is then used as an estimate of the model's performance.

The benefits of using cross-validation include:

  1. More reliable performance estimation: Cross-validation provides a more robust estimate of the model's performance by averaging the results across multiple folds. This helps to reduce the impact of data variability and random chance.

  2. Effective hyperparameter tuning: Cross-validation allows for efficient hyperparameter tuning. By evaluating the model on different folds with different hyperparameter settings, it becomes easier to find the optimal combination of hyperparameters.

  3. Model selection: Cross-validation can aid in selecting the best model among multiple candidate models. By comparing the performance of different models on the validation sets, you can choose the model that generalizes the best.

Overall, cross-validation is a valuable technique in machine learning for assessing model performance, tuning hyperparameters, and selecting the best model. It helps to provide a more accurate estimation of how well the model is expected to perform on unseen data.


Here is some code for the for the implementation of Cross Validation

from sklearn.model_selection import cross_val_scor
  # Multiply by -1 since sklearn calculates negative MAE
  scores = -1 * cross_val_score(my_pipeline, X, y, cv=5,                                                       scoring='neg_mean_absolute_error')
print("Average MAE score:", scores.mean())

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