The correct answer is: B. are better able to deal with missing and noisy data.
Machine learning techniques are better able to deal with missing and noisy data than statistical techniques because they are not based on any assumptions about the data. This means that they can learn from data that is incomplete or inaccurate, which can be very useful in real-world applications.
Statistical techniques, on the other hand, are based on assumptions about the data. These assumptions can be about the distribution of the data, the relationships between variables, or the presence of outliers. If these assumptions are not met, then the results of the statistical analysis can be unreliable.
For example, let’s say you want to predict the price of a house. You could use a statistical technique to fit a model to the data of houses that have already sold. However, this model would only be accurate if the data is representative of all houses on the market. If there are a lot of missing values in the data, or if the data is noisy, then the model will not be very accurate.
On the other hand, you could use a machine learning technique to predict the price of a house. Machine learning techniques can learn from data that is incomplete or inaccurate, so they would be able to produce a more accurate prediction than a statistical technique.
In addition, machine learning techniques are often more robust to outliers than statistical techniques. Outliers are data points that are very different from the rest of the data. They can cause problems for statistical techniques, because they can make the model fit the data too closely and not generalize well to new data. Machine learning techniques are less likely to be affected by outliers, because they are not based on any assumptions about the data.
Overall, machine learning techniques are better able to deal with missing and noisy data than statistical techniques. This is because they are not based on any assumptions about the data, and they are often more robust to outliers.