AI-900: What is the Correct Way to Split Data for Training and Evaluating Machine Learning Models?

Learn the proper technique for splitting data into training and evaluation sets when testing machine learning models. Prepare for the AI-900 Microsoft Azure AI Fundamentals certification exam.

Question

You are testing a machine learning model. How should you split data for training and evaluation?

A. Use features for training and labels for evaluation.
B. Randomly split the data into columns for training and columns for evaluation.
C. Use labels for training and features for evaluation.
D. Randomly split the data into some rows for training and remaining rows for evaluation.

Answer

When testing a machine learning model, the correct approach is to randomly split the data into some rows for training the model and the remaining rows for evaluating its performance (Option D).

D. Randomly split the data into some rows for training and remaining rows for evaluation.

Explanation

It’s essential to use the same features (input variables) for both the training and evaluation datasets. The model learns patterns and relationships from the features during training. Then its predictions are compared to the known labels (output values) in the evaluation set to assess accuracy.

Randomly splitting the data into separate training and evaluation rows, rather than columns, ensures that:

1. The model doesn’t “peek” at the evaluation data during training, which could lead to overfitting and inflated performance metrics.
2. Both datasets are representative samples of the overall data, containing similar distributions of features and labels. This allows for a fair and realistic evaluation.

Options A and C are incorrect because labels are needed for both training the model and evaluating it – not one or the other. And Option B wouldn’t work because the model needs all relevant features, not just a subset, for training and evaluation.

In summary, randomly partitioning the data into distinct training and evaluation row sets, while keeping all columns intact, is the proper way to split data for testing machine learning models. This fundamental skill is critical for AI practitioners and is tested on the AI-900 Microsoft Azure AI Fundamentals certification exam.

The correct way to split data for training and evaluation in a machine learning context is to randomly divide the dataset into two parts: one for training the model and the other for evaluating its performance. This ensures that the model is tested on data it hasn’t seen during training, providing an unbiased assessment of its performance.

Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Fundamentals AI-900 exam and earn Microsoft Azure AI Fundamentals AI-900 certification.