cross_val_score() missing 1 ree

The `cross_val_score()` function from the scikit-learn library requires at least two parameters: an estimator object and the input data. The error you are seeing might be due to missing one of the required parameters.

Here's an example of how to use `cross_val_score()` correctly:

from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LinearRegression
from sklearn.datasets import load_boston

# load the Boston housing dataset
boston = load_boston()

# create a linear regression model
model = LinearRegression()

# get the input data and target values
X = boston.data
y = boston.target

# perform cross-validation and get the scores
scores = cross_val_score(model, X, y, cv=5)

In the example above, we first load the Boston housing dataset using the `load_boston()` function. Then, we create a linear regression model using `LinearRegression()` and get the input data and target values from the dataset. Finally, we use `cross_val_score()` to perform a 5-fold cross-validation and get the scores. Note that we pass the `model`, `X`, and `y` parameters to `cross_val_score()`, and we set the `cv` parameter to 5, which means we want to perform 5-fold cross-validation.