Practice Questions
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Which of the following statements best describes the relationship between overfitting and underfitting in the context of loss functions?
Overfitting means the model learns the noise in the training data, so it works well on that data but poorly on new data. Other options are incorrect b...
Which type of loss function incorporates regularization to prevent overfitting in a machine learning model?
Lasso Loss adds an L1 regularization term to the usual loss. Other options are incorrect because Mean Squared Error Loss only measures the average squ...
In the context of evaluating predictive models, how do mean squared error (MSE) and mean absolute error (MAE) differ in terms of sensitivity to outliers?
MSE squares each error, so a big mistake becomes much larger. Other options are incorrect because Some think squaring makes the error smaller, but it ...
In a regression model, you are evaluating the performance of your predictions using the mean absolute error (MAE). If you notice that the MAE is significantly lower than the root mean square error (RMSE) for the same model, what can you infer about the distribution of the errors in your predictions?
RMSE squares each error, so large mistakes get a lot of weight. Other options are incorrect because Assuming symmetry would make MAE and RMSE close; L...
In the context of machine learning, how does cross-entropy loss serve as an effective loss function for model evaluation metrics, particularly in classification tasks?
Cross‑entropy compares the predicted probability distribution to the true distribution. Other options are incorrect because Some think cross‑entropy i...
Mean Squared Error : Predictive Accuracy :: Cross-Entropy : ?
Cross‑entropy measures how well a model predicts the right class. Other options are incorrect because Loss functions do not tell how many parameters a...
In the context of loss functions, the _____ is a method used to minimize the difference between predicted values and actual values by adjusting model parameters.
The method picks model settings that lower the average error between predictions and real outcomes. Other options are incorrect because People sometim...
In selecting a loss function for a regression model, which characteristic is most crucial for ensuring that outliers do not disproportionately influence the model's performance?
Absolute loss (L1) adds a penalty that grows linearly with error size. Other options are incorrect because A complex model can fit many points, but it...
Which of the following statements accurately describe loss functions in machine learning? Select all that apply.
Loss functions compare what the model predicts to the true answer. Other options are incorrect because Many people think every loss function is a stra...
A model is being trained to predict housing prices based on various features like square footage, location, and number of bedrooms. The model uses the Mean Squared Error (MSE) as its loss function. Which of the following scenarios best illustrates a situation where the use of MSE would be inappropriate, and why?
When a few very expensive houses stand out from the rest, MSE squares the errors. Other options are incorrect because A balanced spread of prices does...
A data scientist is developing a machine learning model to predict house prices based on features like size, location, and number of bedrooms. After training the model, they notice that the predictions are consistently higher than the actual prices. They decide to use a loss function to evaluate their model's performance. Which loss function would be most appropriate for penalizing these discrepancies effectively?
This loss squares the error between prediction and reality. Other options are incorrect because This loss adds the absolute difference; This loss is f...
Which of the following best describes the role of loss functions in predictive modeling?
Loss functions give a number that shows how far predictions are from the real values. Other options are incorrect because Some think loss functions de...
Arrange the following steps in the process of evaluating a loss function in empirical risk minimization: A) Compute the predicted values using the predictor function, B) Determine the actual output values, C) Calculate the loss by comparing predicted and actual values, D) Use the loss to update the model parameters.
First the model makes predictions. Other options are incorrect because This order starts with the real answers before predictions; Here the loss is co...
If a machine learning model consistently underperforms on its predictions, which underlying factor is most likely contributing to this issue?
The loss function tells the model what to improve. Other options are incorrect because Overfitting means the model is too tuned to the training data; ...
Master Loss Functions
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