Discover the key steps a data scientist must take to enhance the accuracy of a machine learning model for predicting house prices. Learn how to optimize regularization, training data, and feature selection to achieve better results.
Table of Contents
Question
A data scientist wants to improve the fit of a machine learning (ML) model that predicts house prices. The data scientist makes a first attempt to fit the model, but the fitted model has poor accuracy on both the training dataset and the test dataset.
Which steps must the data scientist take to improve model accuracy? (Choose three.)
A. Increase the amount of regularization that the model uses.
B. Decrease the amount of regularization that the model uses.
C. Increase the number of training examples that that model uses.
D. Increase the number of test examples that the model uses.
E. Increase the number of model features that the model uses.
F. Decrease the number of model features that the model uses.
Answer
A. Increase the amount of regularization that the model uses.
C. Increase the number of training examples that that model uses.
E. Increase the number of model features that the model uses.
Explanation
To improve the accuracy of a machine learning model that predicts house prices, a data scientist should consider the following steps:
(A) Increase the amount of regularization that the model uses. Regularization techniques, such as L1 (Lasso) or L2 (Ridge) regularization, can help prevent overfitting by adding a penalty term to the model’s loss function. This encourages the model to learn simpler and more generalized patterns, reducing the risk of fitting noise in the training data.
(C) Increase the number of training examples that the model uses. By providing more diverse and representative training data, the model can learn better patterns and relationships, leading to improved accuracy on both the training and test datasets.
(E) Increase the number of relevant model features that the model uses. Including additional informative features related to house prices, such as square footage, number of bedrooms, location, and amenities, can help the model capture more complex relationships and improve its predictive power.
By combining these steps – increasing the training data, incorporating relevant features, and applying appropriate regularization – the data scientist can improve the model’s accuracy and generalization capabilities, leading to better house price predictions on both the training and test datasets.
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