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  "title_listed": "List of Evaluation Metrics for Classification and Regression",
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  "title": "List of Evaluation Metrics for Classification and Regression - DEV Community",
  "body_markdown": "Machine Learning is the science of teaching computers to perform certain tasks without being explicitly programmed. It can be basically divided into 3 parts-\nSupervised Learning\nUnsupervised Learning\nReinforcement Learning\nSupervised learning is the part that is majorly taught everywhere and consists of two very important tasks\nClassification\nand\nRegression\n. To know more about these tasks and a simple introduction to how to perform them using Python read my article given below.\nBasic Machine Learning Cheatsheet using Python [10 Classification & Regression Methods]\namananandrai ・ Jun 18 '20\n#machinelearning\n#datascience\n#beginners\n#python\nWe need to assess the performance of our machine learning models and here I am giving a list of evaluation metrics for classification and regression tasks.\nEvaluation Metrics for Classification\nAccuracy\nPrecision (P)\nRecall (R)\nF1 score (F1)\nArea under the ROC (Receiver Operating Characteristic) curve or simply Area Under Curve (AUC)\nLog loss\nPrecision at k (P@k)\nAverage precision at k (AP@k)\nMean average precision at k (MAP@k)\nEvaluation Metrics for Regression\nMean absolute error (MAE)\nMean squared error (MSE)\nRoot mean squared error (RMSE)\nRoot mean squared logarithmic error (RMSLE)\nMean percentage error (MPE)\nMean absolute percentage error (MAPE)\nR-square (R^2)\nFor a much more detailed explanation of Performance metrics you can read the article given below.\nhttps://neptune.ai/blog/performance-metrics-in-machine-learning-complete-guide",
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