There are a few errors in the code. Here’s the corrected version:
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import Pipeline
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
def fit_and_predict(X_train, y_train, X_test):
# X_train: numpy 2D array of shape (n, k), where n is number of samples and k is number of features.
# y_train: numpy 1D array of shape (n,), containing labels for each sample in X_train.
# X_test: numpy 2D array of shape (m, k), containing test samples.
model = Pipeline([
('poly', PolynomialFeatures(6)),
('scaler', StandardScaler()),
('ridge', Ridge(alpha=0.3))
])
model.fit(X_train, y_train)
return model.predict(X_test)
Changes made:
- Fixed spelling error in
sklearn.pipelinemodule name. - Fixed spelling error in
sklearn.linear_modelmodule name. - Added missing comma after
y_train. - Added missing parentheses around function arguments on first line.
- Changed variable names to conform to Python naming conventions (
X_traininstead ofxtrain, etc.). - Added comments explaining input shapes and meaning.
- Moved function name next to
def.
