Which Patsy function is used to apply stateful transformations to new, out-of-sample data using the saved information from an original in-sample dataset?
Explanation
This question tests the user's knowledge of the specific Patsy function needed to correctly preprocess new data in a way that is consistent with the original training data.
Other questions
What is the primary method described for turning a pandas DataFrame into a NumPy array, which serves as the point of contact between pandas and other analysis libraries?
What is the result when the to_numpy method is used on a DataFrame containing heterogeneous data, such as a mix of numeric types and strings?
What is the recommended approach for converting only a subset of a DataFrame's columns into a NumPy array?
Which pandas function is used to convert a categorical variable into 'dummy' or 'indicator' variables?
What is the primary purpose of the Patsy library as described in the chapter?
In the Patsy formula syntax 'y ~ x0 + x1', what does the plus symbol (+) signify?
When using `patsy.dmatrices('y ~ x0 + x1', data)`, what additional term is typically included in the resulting design matrix X by default?
How can you prevent Patsy from automatically adding an intercept term to a model's design matrix?
What are 'stateful transformations' in the context of Patsy, and why do they require special handling for new data?
How can you instruct Patsy to treat a numeric column as a categorical variable when creating dummy variables?
What are the two main interfaces provided by the statsmodels library for fitting linear models?
When using the array-based interface in statsmodels (e.g., `sm.OLS`), what function is typically used to add an intercept column to an existing matrix of predictors?
In statsmodels, after fitting a model using the `.fit()` method, what does the `.summary()` method on the results object provide?
What is a key advantage of using the statsmodels formula API (`smf`) with a pandas DataFrame, as demonstrated in the chapter?
In the scikit-learn example using the Titanic dataset, how were the missing values in the 'Age' column handled before fitting the model?
Which scikit-learn method is used to train a model on a training dataset?
What is the primary purpose of cross-validation in model training, as described in the chapter?
Which scikit-learn helper function is shown to perform cross-validation by handling the data splitting process and returning scores for each split?
When creating a model for the Titanic dataset, the 'Sex' column was converted into an 'IsFemale' column. How was this encoding performed?
In the Patsy formula 'v2 ~ key1 + key2 + key1:key2', what does the term 'key1:key2' represent?
Which class from `statsmodels.tsa.ar_model` is used to fit an autoregressive time series model?
In the `cross_val_score(model, X_train, y_train, cv=4)` example, how many scores are returned in the resulting array?
What is the primary distinction between the kinds of models found in statsmodels versus other libraries mentioned, like scikit-learn?
When using `patsy.dmatrices` with a nonnumeric term like `'key1'` which has categories 'a' and 'b', and an intercept is included, how is the term represented in the design matrix?
How can you convert a two-dimensional ndarray back to a pandas DataFrame with specified column names?
What does the Patsy function `I()` allow you to do within a formula string?
After fitting a statsmodels OLS model with the formula API on a DataFrame, what is the data type of the `results.params` attribute?
How do you obtain predicted values for new, out-of-sample data using a fitted statsmodels model?
According to the chapter, what is a key difference in the API for logistic regression between scikit-learn's `LogisticRegression` and `LogisticRegressionCV`?
In the autoregressive model example `model = AutoReg(values, MAXLAGS)`, what does the `MAXLAGS` argument represent?
What is the first value in the `results.params` array for the fitted `AutoReg` model in the statsmodels example?
In scikit-learn, what is the standard method to obtain predictions on a test dataset (`X_test`) from a fitted model instance (`model`)?
Based on the code snippet `data['category'] = pd.Categorical(['a', 'b', 'a', 'a', 'b'], categories=['a', 'b'])`, what is the purpose of the `categories` argument?
In the example where a DataFrame `df3` with numeric and string columns is converted using `df3.to_numpy()`, what is the resulting array's `dtype`?
In the Patsy formula `y ~ standardize(x0) + center(x1)`, what is the effect of the `center(x1)` transformation?
What is the key difference between the formula `y ~ x0 + x1` and `y ~ x0 * x1` in Patsy?
When fitting the initial Ordinary Least Squares model in the statsmodels section (`model = sm.OLS(y, X)`), why was the model fit without an explicit intercept term in the call?
In the Patsy example, after fitting a model with `np.linalg.lstsq(X, y)`, how are the model column names reattached to the resulting coefficient array?
In the scikit-learn example `model.fit(X_train, y_train)`, what does `X_train` represent?
What workflow is described as common for model development in the first paragraph of Chapter 12.1?
Based on the code `dummies = pd.get_dummies(data.category, prefix='category')`, what is the purpose of the `prefix` argument?
What type of library is Patsy described as being inspired by?
What is the result of running the code `(y_true == y_predict).mean()` in the scikit-learn section?
Why might it be simpler and less error-prone to use Patsy when you have more than simple numeric columns?
What are the three predictors used to create the `X_train` NumPy array for the Titanic survival model?
When the formula API of statsmodels (`smf.ols`) is used with the formula 'y ~ col0 + col1 + col2', what does the resulting `results.tvalues` attribute contain?
In the scikit-learn section, what is the default scoring metric for `cross_val_score` described as being dependent on?
What type of data is the `to_numpy` method primarily intended for, according to the text?
When creating a logistic regression model in scikit-learn with `model = LogisticRegression(C=10)`, what does the `C` parameter typically control?