AI-900: How Do You Evaluate a Linear Regression Model?

Learn the key method for evaluating linear regression models for the Microsoft Azure AI-900 certification exam. Get insights on data splitting and training techniques.

Question

What is the typical method of evaluating the performance of a linear regression model?

A. Create new output values using a previously used classification model.
B. Use a sigmoid function to compute output values.
C. Run a sigmoid function on the model output values.
D. Split the data and then use training data with known output values.

Answer

D. Split the data and then use training data with known output values.

Explanation

You will split the data and then use training data with known output values. This is a part of hold-out validation which is a common technique for evaluating machine learning models, including linear regression. This process is as follows:

1. Split the data: Divide your data into two sets: training and testing. The training set is used to train the model, while the testing set is used to evaluate its performance on unseen data.
2. Train the model: Use the training data to fit the linear regression model.
3. Make predictions on the testing data: Use the trained model to predict the output values for the testing data points.
4. Calculate evaluation metrics: Compare the predicted values with the actual known values in the testing data using metrics such as mean squared error (MSE) or R-squared. These metrics indicate if the model generalizes to unseen data and avoids overfitting the training data.

By comparing the model’s predictions on the testing set with the actual known values, we can assess the model’s performance on unfamiliar data. This helps prevent overfitting, where the model simply memorizes the training data and performs poorly on new examples.

You would not create new output values using a previously used classification model. This approach does not align with the evaluation process of a linear regression model. Regression models deal with predicting continuous numerical values, while classification models categorize data points into discrete classes. Combining these functionalities would not provide meaningful evaluation for a linear regression model.

You would not use a sigmoid function to compute output values. Sigmoid functions are typically used in logistic regression models, not linear regression models. They are used to transform outputs into probabilities between 0 and 1, which is not the goal of linear regression.

You would not run a sigmoid function on the model output values. Sigmoid functions are not suitable for linear regression.

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