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Calibration training is a process used to align the outputs of a model or measurement system with known standards or ground truth, ensuring accuracy and reliability. It is essential in fields like machine learning and quality control to minimize bias and improve predictive performance.
The Continuous Ranked Probability Score (CRPS) is a metric used to evaluate the accuracy of probabilistic forecasts by comparing the cumulative distribution function of the forecast to the observed outcome. It provides a single score that measures both the reliability and resolution of the forecast, making it a comprehensive tool for assessing predictive performance in various domains, such as meteorology and finance.
Cross-validation for time series is a method used to assess the predictive performance of models while accounting for temporal dependencies in the data. It involves techniques like rolling forecasting origin and time series split to ensure that the training and test sets respect the chronological order of observations.
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Ensemble learning is a machine learning paradigm where multiple models, often referred to as 'weak learners', are combined to produce a stronger, more accurate model. This approach leverages the diversity among individual models to reduce overfitting and improve predictive performance by aggregating their predictions through techniques like bagging, boosting, or stacking.
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The holdout method is a simple and commonly used technique for evaluating the performance of machine learning models by splitting the dataset into separate training and testing sets. This approach helps prevent overfitting by ensuring that the model is tested on unseen data, providing a more realistic assessment of its predictive capabilities.
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Perplexity is a measurement used to evaluate the performance of probabilistic models, particularly in natural language processing, indicating how well a probability distribution predicts a sample. Lower perplexity values suggest better predictive performance and alignment with the actual distribution of the data.
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K-Fold Cross Validation is a robust method for assessing the predictive performance of a machine learning model by partitioning the dataset into 'k' subsets, or folds, and iteratively training and validating the model 'k' times, each time using a different fold as the validation set and the remaining folds as the training set. This technique helps in reducing overfitting and provides a more generalized evaluation of the model's performance by averaging the results across all folds.
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