In comparison to mean absolute error, mean squared error:1 pointIs more interpretable by taking the same unit as the response.Focuses more on large errors.Weighs small and large errors equally.­Avoids cancellation of errors.

The statement "In comparison to mean absolute error, mean squared error" can be explained as follows:

1. "Is more interpretable by taking the same unit as the response." - This is incorrect. Mean Squared Error (MSE) is not more interpretable by taking the same unit as the response. In fact, it squares the unit of the response, which can make it less interpretable.

2. "Focuses more on large errors." - This is correct. MSE squares the errors before they are averaged, which gives more weight to larger errors. This means that MSE is more sensitive to outliers than Mean Absolute Error (MAE).

3. "Weighs small and large errors equally." - This is incorrect. As mentioned above, MSE gives more weight to larger errors because it squares the errors before they are averaged.

4. "Avoids cancellation of errors." - This is correct. Both MSE and MAE avoid cancellation of errors because they use absolute values or squares of the errors, which are always positive.

The statement "In comparison to mean absolute error, mean squared error" can be explained as follows:

1. Is more interpretable by taking the same unit as the response: This statement is incorrect. Mean Squared Error (MSE) is not more interpretable by taking the same unit as the response. In fact, it squares the unit of the response, which can make it less interpretable.

2. Focuses more on large errors: This statement is correct. MSE squares the errors before they are averaged, which gives more weight to larger errors. This means that MSE is more sensitive to outliers and large errors.

3. Weighs small and large errors equally: This statement is incorrect. As mentioned above, MSE squares the errors, which gives more weight to larger errors. Therefore, it does not weigh small and large errors equally.

4. Avoids cancellation of errors: This statement is correct. By squaring the errors, MSE ensures that positive and negative errors do not cancel each other out. This is a feature that Mean Absolute Error (MAE) also has, but it is still a correct statement about MSE.