You are trying to improve a linear regression model by adding polynomial features. After adding the polynomial features, you observe that the model performs much better on the training set but worse on the validation set. What is likely happening?

You are working with a small dataset and want to assess your model's performance. Your colleague recommends using k-fold cross-validation. However, you are concerned about the reliability of the evaluation. What cross-validation technique would be most appropriate for your small dataset?

In a given dataset, you have a categorical feature "State" that includes the 50 U.S. states. You decide to one-hot encode this feature. What potential issue should you be cautious about?

You are tasked with merging two datasets, df1 and df2, on a common column 'id'. df1 has a column 'value1' and df2 has a column 'value2'. After merging, you need to create a new column called 'total_value' that sums 'value1' and 'value2'. Which of the following code snippets accomplishes this?

Your dataset consists of geographical data points for different species of birds. You want to visualize the distribution of these species on a geographical map. Which Python library would be most appropriate for this kind of visualization?