lib/rumale/ensemble/stacking_regressor.rb
# Fit the model with training data, and then transform them with the learned model.
#
# @param x [Numo::DFloat] (shape: [n_samples, n_features]) The training data to be used for fitting the model.
# @param y [Numo::DFloat] (shape: [n_samples, n_outputs]) The target variables to be used for fitting the model.
# @return [Numo::DFloat] (shape: [n_samples, n_components]) The meta features for training data.
Fit the model with training data, and then transform them with the learned model.
Fit the model with training data and then transform them with the learned model .
def fit_transform(x, y)
        x = check_convert_sample_array(x)
        y = check_convert_tvalue_array(y)
        fit(x, y).transform(x)
      end
def fit_transform ( x , y ) x = check_convert_sample_array ( x ) y = check_convert_tvalue_array ( y ) fit ( x , y ) . transform ( x ) end
Fit the model with training data, and then transform them with the learned model.
Fit the model with training data and then transform them with the learned model .
[n_samples, n_components]) The meta features for training data.
[ n_samples n_components ] ) The meta features for training data .
Numo::DFloat
x
[n_samples, n_features]) The training data to be used for fitting the model.
[ n_samples n_features ] ) The training data to be used for fitting the model .
y
[n_samples, n_outputs]) The target variables to be used for fitting the model.
[ n_samples n_outputs ] ) The target variables to be used for fitting the model .