There are 10 dice in the box. One of the dice is defective, meaning it has unequal chances of rolling each side. The chances to roll each side for it are the following:
- Side 1: 10%
- Side 2: 10%
- Side 3: 10%
- Side 4: 20%
- Side 5: 20%
- Side 6: 30%
Other dice in the box are not defective, meaning rolling each side has a probability of 1/6.
You randomly select two dice from the box and roll them. Calculate the expected sum of the values you will roll. Round your answer to the nearest thousandth (three decimal places, e.g., 1.234).
A scientist is carrying out a series of experiments. Each experiment can end with either a success or a failure. The probability of success is p = 0.820, and the probability of failure is q = 0.180. Experiments in a series are independent of one another.
If an experiment ends with a success, the detector registers its results correctly with a probability of pr = 0.960. If the experiment ends with a failure, nothing is registered.
The scientist is going to run a series of 20 experiments. Calculate the probability of getting exactly 16 experiment results registered correctly on the detector. Round your answer to the nearest thousandth (three decimal places, e.g., 1.234).
Name reasons to use MSE over MAE when training a regression model. Note, that target variable is represented as a car price in $ and is not to be scaled.
--MSE function is continuous, while MAE function is discontinuous.
-- MSE metric Is more sensitive to Blas of the error than the MAE metric.
-- MSE function is a continuously differentiable function, while MAE function is not, which makes calculations with MSE metric easier to implement.
--MSE function is a convex function, while MAE function is not, which makes calculations with MSE metric easier to implement.. 1point3acres
-- MSE metric applies greater penalty for greater errors than MAE metric.
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