ตอนนี้คุณก็พร้อมที่จะดำเนินการขั้นถัดไปในเส้นทาง ML แล้ว
คู่มือ People + AI หากคุณกำลังมองหาชุดวิธีการ แนวทางปฏิบัติแนะนำ และตัวอย่างที่นำเสนอโดย Googler, ผู้เชี่ยวชาญในอุตสาหกรรม และการวิจัยทางวิชาการสําหรับการใช้ ML
การกำหนดปัญหา หากคุณกําลังมองหาแนวทางที่ผ่านการทดสอบภาคสนามสําหรับการสร้างโมเดล ML และหลีกเลี่ยงข้อผิดพลาดที่พบบ่อย
[[["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 2025-07-27 UTC"],[[["\u003cp\u003eThis page tests your understanding of core machine learning (ML) concepts through interactive questions.\u003c/p\u003e\n"],["\u003cp\u003eIt covers fundamental ML topics such as predictive power of features, supervised and unsupervised learning, and model training and evaluation.\u003c/p\u003e\n"],["\u003cp\u003eYou'll learn how to choose the right ML approach for different problems and assess the effectiveness of a trained model.\u003c/p\u003e\n"],["\u003cp\u003eLinks to further resources are provided to deepen your understanding of ML and its practical applications.\u003c/p\u003e\n"]]],[],null,["# Test Your Understanding\n\n\u003cbr /\u003e\n\nThe following questions help you solidify your understanding of core ML concepts.\n\nPredictive power\n----------------\n\nSupervised ML models are trained using datasets with labeled examples. The model\nlearns how to predict the label from the features. However, not every feature in\na dataset has predictive power. In some instances, only a few features act as\npredictors of the label. In the dataset below, use price as the label\nand the remaining columns as the features.\n\nWhich three features do you think are likely the greatest predictors for a car's price? \nMake_model, year, miles. \nA car's make/model, year, and miles are likely to be among the strongest predictors for its price. \nColor, height, make_model. \nA car's height and color are not strong predictors for a car's price. \nMiles, gearbox, make_model. \nThe gearbox isn't a main predictor of price. \nTire_size, wheel_base, year. \nTire size and wheel base aren't strong predictors for a car's price.\n\nSupervised and unsupervised learning\n------------------------------------\n\nBased on the problem, you'll use either a supervised or unsupervised approach.\nFor example, if you know beforehand the value or category you want to predict,\nyou'd use supervised learning. However, if you wanted to learn if your dataset\ncontains any segmentations or groupings of related examples, you'd use\nunsupervised learning.\n\nSuppose you had a dataset of users for an online shopping website, and it contained the following columns:\n\nIf you wanted to understand the types of users that visit the site, would you use supervised or unsupervised learning? \nUnsupervised learning. \nBecause we want the model to cluster groups of related customers, we'd use unsupervised learning. After the model clustered the users, we'd create our own names for each cluster, for example, \"discount seekers,\" \"deal hunters,\" \"surfers,\" \"loyal,\" and \"wanderers.\" \nSupervised learning because I'm trying to predict which class a user belongs to. \nIn supervised learning, the dataset must contain the label you're trying to predict. In the dataset, there is no label that refers to a category of user.\n\nSuppose you had an energy usage dataset for homes with the following columns:\n\nWhat type of ML would you use to predict the kilowatt hours used per year for a newly constructed house? \nSupervised learning. \nSupervised learning trains on labeled examples. In this dataset \"kilowatt hours used per year\" would be the label because this is the value you want the model to predict. The features would be \"square footage,\" \"location,\" and \"year built.\" \nUnsupervised learning. \nUnsupervised learning uses unlabeled examples. In this example, \"kilowatt hours used per year\" would be the label because this is the value you want the model to predict.\n\nSuppose you had a flight dataset with the following columns:\n\nIf you wanted to predict the cost of an airplane ticket, would you use regression or classification? \nRegression \nA regression model's output is a numeric value. \nClassification \nA classification model's output is a discrete value, normally a word. In this case, the cost of an airplane ticket is a numeric value. \nBased on the dataset, could you train a classification model to classify the cost of an airplane ticket as \"high,\" \"average,\" or \"low\"? \nYes, but we'd first need to convert the numeric values in the `airplane_ticket_cost` column to categorical values. \nIt's possible to create a classification model from the dataset. You would do something like the following:\n\n1. Find the average cost of a ticket from the departure airport to the destination airport.\n2. Determine the thresholds that would constitute \"high,\" \"average,\" and \"low\".\n3. Compare the predicted cost to the thresholds and output the category the value falls within. \nNo. It's not possible to create a classification model. The `airplane_ticket_cost` values are numeric not categorical. \nWith a little bit of work, you could create a classification model. \nNo. Classification models only predict two categories, like `spam` or `not_spam`. This model would need to predict three categories. \nClassification models can predict multiple categories. They're called multiclass classification models.\n\nTraining and evaluating\n-----------------------\n\nAfter we've trained a model, we evaluate it by using a dataset with labeled examples\nand compare the model's predicted value to the label's actual value.\n\nSelect the two best answers for the question. \nIf the model's predictions are far off, what might you do to make them better? \nRetrain the model, but use only the features you believe have the strongest predictive power for the label. \nRetraining the model with fewer features, but that have more predictive power, can produce a model that makes better predictions. \nYou can't fix a model whose predictions are far off. \nIt's possible to fix a model whose predictions are off. Most models require multiple rounds of training until they make useful predictions. \nRetrain the model using a larger and more diverse dataset. \nModels trained on datasets with more examples and a wider range of values can produce better predictions because the model has a better generalized solution for the relationship between the features and the label. \nTry a different training approach. For example, if you used a supervised approach, try an unsupervised approach. \nA different training approach would not produce better predictions.\n\nYou're now ready to take the next step in your ML journey:\n\n- [People + AI Guidebook](https://pair.withgoogle.com/guidebook/). If you're\n looking for a set of methods, best practices and examples presented by\n Googlers, industry experts, and academic research for using ML.\n\n- [Problem Framing](/machine-learning/problem-framing). If you're looking for\n a field-tested approach for creating ML models and avoiding common pitfalls\n along the way.\n\n- [Machine Learning Crash Course](/machine-learning/crash-course). If you're\n ready for an in-depth and hands-on approach to learning more about ML."]]
