AI-900: How to Avoid Common Mistakes When Training Supervised Models?

Discover key strategies for training supervised machine learning models in Azure AI-900 exams. Learn proper data splitting techniques, validation methods, and evaluation best practices to ace Microsoft’s certification.

Question

Which of the following is the correct approach to use when training a supervised machine learning model?

A. Use different subsets of labeled data for training and evaluation.
B. Use labeled data with unknown values for both the label and the features.
C. Assess model performance by comparing predicted new values with older predicted values.
D. Use all the available data to fit a function that relates features and the label.

Answer

A. Use different subsets of labeled data for training and evaluation.

Explanation

Using different subsets of labeled data for training and evaluation is the correct approach for the training and evaluation of a supervised machine learning model. Splitting the labeled data into separate training and evaluation sets is a crucial step in supervised machine learning. The process for this is as follows:

• Data Splitting: The available labeled data is divided into two subsets:
  • Training set: Used to train the model and build the relationship between the features and the label.
  • Evaluation (or validation) set: Used to assess the model’s performance on unseen data.
• Training: The model is trained using the training set, learning the patterns and relationships within the data.
• Evaluation: The model’s performance is evaluated on the evaluation set, which it has not seen before. This provides an unbiased assessment of how well the model generalizes to new data and avoids overfitting.

You would not use labeled data with unknown values for both the label and the features. This scenario would not be possible for training purposes. Supervised learning requires labeled data, meaning both the features and the label must have known values.

You would not assess model performance by comparing predicted new values with older predicted values. While comparing new predictions with past predictions can provide some insights, it is not a reliable approach for evaluating model performance. Ideally, you need to compare predictions with actual values not previously used in training.

You would not use all the available data to fit a function that relates the features and the label. This approach, known as overfitting, can lead to a model that performs well on the training data but fails to generalize to unseen data. Splitting the data into training and evaluation sets prevents overfitting and ensures that the model can learn the underlying relationships effectively.

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