I make this code

# Numeric Pipeline
num_pipeline = make_pipeline(RobustScaler(), PCA(n_components=0.95))

# Categorical Pipeline
cat_pipeline = make_pipeline(OneHotEncoder(handle_unknown="ignore"))

# Creating Column Transformer
preprocessing_pipeline = ColumnTransformer([
    ('num', num_pipeline, num_columns),
    ('cat', cat_pipeline, cat_columns)
])

# Full Pipeline
full_pipeline = make_pipeline(preprocessing_pipeline, LinearRegression())

# Tuning Parameters with GridSearchCV
param_grid = {
    'linearregression__fit_intercept': [True, False],  # Whether to calculate the intercept for this model
}

grid_LR = GridSearchCV(full_pipeline, param_grid, cv=6)

# Get the best linear regression model from the grid search
best_linear_reg_model = grid_LR.best_estimator_

from sklearn.ensemble import AdaBoostRegressor

# Create AdaBoostRegressor with the best linear regression model as the base estimator
ada_reg = AdaBoostRegressor(estimator=best_linear_reg_model, random_state=42)

# Fit Model
ada_reg.fit(X_train_overall, y_train)

I got this error

AttributeError: ‘numpy.ndarray’ object has no attribute ‘columns’
ValueError: Specifying the columns using strings is only supported for dataframes.

Fix this code or suggestion