Evaluation of Machine Learning Models with Scikit-learn: Metrics and Cross-Validation – vegibit

In machine learning, model evaluation is the process of evaluating the performance of a model on a given dataset. It is an essential step in the machine learning pipeline as it helps to determine the effectiveness of a model and identify areas for improvement. Model evaluation can be performed using various metrics, such as accuracy, precision, recall, and F1 score, which provide different insights into the model's performance. Additionally, techniques such as cross-validation can be used to assess the generalization performance of a model and prevent overfitting. This article will explore metrics and cross-validation for evaluating machine learning models with the scikit-learn library.