[[["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"],[[["Like sorting good apples from bad, ML engineers spend significant time cleaning data by removing or fixing bad examples to improve dataset quality."],["Common data problems include omitted values, duplicate examples, out-of-range values, and incorrect labels, which can negatively impact model performance."],["You can use programs or scripts to identify and handle data issues such as omitted values, duplicates, and out-of-range feature values by removing or correcting them."],["When multiple individuals label data, it's important to check for consistency and identify potential biases to ensure label quality."],["Addressing data quality issues before training a model leads to better model accuracy and overall performance."]]],[]]
