[[["เข้าใจง่าย","easyToUnderstand","thumb-up"],["แก้ปัญหาของฉันได้","solvedMyProblem","thumb-up"],["อื่นๆ","otherUp","thumb-up"]],[["ไม่มีข้อมูลที่ฉันต้องการ","missingTheInformationINeed","thumb-down"],["ซับซ้อนเกินไป/มีหลายขั้นตอนมากเกินไป","tooComplicatedTooManySteps","thumb-down"],["ล้าสมัย","outOfDate","thumb-down"],["ปัญหาเกี่ยวกับการแปล","translationIssue","thumb-down"],["ตัวอย่าง/ปัญหาเกี่ยวกับโค้ด","samplesCodeIssue","thumb-down"],["อื่นๆ","otherDown","thumb-down"]],["อัปเดตล่าสุด 2024-08-13 UTC"],[[["\u003cp\u003ePrediction bias, calculated as the difference between the average prediction and the average ground truth, is a quick check for model or data issues.\u003c/p\u003e\n"],["\u003cp\u003eA model with zero prediction bias ideally predicts the same average outcome as observed in the ground truth data, such as a spam detection model predicting the same percentage of spam emails as actually present in the dataset.\u003c/p\u003e\n"],["\u003cp\u003eSignificant prediction bias can indicate problems in the training data, the model itself, or the new data being applied to the model.\u003c/p\u003e\n"],["\u003cp\u003eCommon causes of prediction bias include biased data, excessive regularization, bugs in the training process, and insufficient features provided to the model.\u003c/p\u003e\n"]]],[],null,["As mentioned in the\n[Linear regression](/machine-learning/crash-course/linear-regression)\nmodule, calculating\n[**prediction bias**](/machine-learning/glossary#prediction_bias)\nis a quick check that can flag issues with the model or training data\nearly on.\n\nPrediction bias is the difference between the mean of a model's\n[**predictions**](/machine-learning/glossary#prediction)\nand the mean of\n[**ground-truth**](/machine-learning/glossary#ground-truth) labels in the\ndata. A model trained on a dataset\nwhere 5% of the emails are spam should predict, on average, that 5% of the\nemails it classifies are spam. In other words, the mean of the labels in the\nground-truth dataset is 0.05, and the mean of the model's predictions should\nalso be 0.05. If this is the case, the model has zero prediction bias. Of\ncourse, the model might still have other problems.\n\nIf the model instead predicts 50% of the time that an email is spam, then\nsomething is wrong with the training dataset, the new dataset the model is\napplied to, or with the model itself. Any\nsignificant difference between the two means suggests that the model has\nsome prediction bias.\n\nPrediction bias can be caused by:\n\n- Biases or noise in the data, including biased sampling for the training set\n- Too-strong regularization, meaning that the model was oversimplified and lost some necessary complexity\n- Bugs in the model training pipeline\n- The set of features provided to the model being insufficient for the task\n\n| **Key terms:**\n|\n| - [Ground truth](/machine-learning/glossary#ground-truth)\n| - [Prediction](/machine-learning/glossary#prediction)\n- [Prediction bias](/machine-learning/glossary#prediction_bias) \n[Help Center](https://support.google.com/machinelearningeducation)"]]
