Model Evaluation
Model evaluation is the process of assessing the performance of a machine learning model on a given dataset. It involves measuring the accuracy, precision, recall, F1 score, and other metrics to determine how well the model is performing. Model evaluation is an essential step in the machine learning pipeline as it helps to identify the strengths and weaknesses of the model and provides insights into how to improve it. There are various techniques for model evaluation, including cross-validation, holdout validation, and bootstrapping. Cross-validation involves dividing the dataset into k-folds and training the model on k-1 folds while testing it on the remaining fold. Holdout validation involves splitting the dataset into training and testing sets, where the model is trained on the training set and evaluated on the testing set. Bootstrapping involves resampling the dataset with replacement to create multiple training and testing sets. Model evaluation is crucial in ensuring that the machine learning model is accurate, reliable, and generalizes well to new data.
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