2d3ec31520f2a6d2187ddb529665d8b984bda45e,examples/cross_validation.py,,,#,30

Before Change

// -----------------------------------------------------------------------------
data = load_diabetes()

icp = IcpRegressor(NormalizedRegressorNc(RegressorAdapter(RandomForestRegressor(n_estimators=100)),
                                         RegressorAdapter(RandomForestRegressor(n_estimators=100)),
                                         AbsErrorErrFunc()))
icp_cv = RegIcpCvHelper(icp)

scores = cross_val_score(icp_cv,
                         data.data,
                         data.target,
                         iterations=5,
                         folds=5,
                         scoring_funcs=[reg_mean_errors, reg_median_size],
                         significance_levels=[0.05, 0.1, 0.2])


print("Normalized absolute error regression: diabetes")
scores = scores.drop(["fold", "iter"], axis=1)
print(scores.groupby(["significance"]).mean())

// -----------------------------------------------------------------------------

After Change

// -----------------------------------------------------------------------------
data = load_diabetes()

underlying_model = RegressorAdapter(RandomForestRegressor(n_estimators=100))
normalizer_model = RegressorAdapter(RandomForestRegressor(n_estimators=100))
normalizer = RegressorNormalizer(underlying_model, normalizer_model, AbsErrorErrFunc())
nc = RegressorNc(underlying_model, AbsErrorErrFunc(), normalizer)

icp = IcpRegressor(nc)
icp_cv = RegIcpCvHelper(icp)

scores = cross_val_score(icp_cv,
                         data.data,
                         data.target,
                         iterations=5,
                         folds=5,
                         scoring_funcs=[reg_mean_errors, reg_median_size],
                         significance_levels=[0.05, 0.1, 0.2])


print("Normalized absolute error regression: diabetes")
scores = scores.drop(["fold", "iter"], axis=1)
print(scores.groupby(["significance"]).mean())

// -----------------------------------------------------------------------------

Instances