Google Colab and Auto-sklearn with Profiling

X, y, coeff = make_regression(
n_samples=1000,
n_features=100,
n_informative=5,
noise=0,
shuffle=False,
coef=True
)
Subset of 100 generated features
import autosklearn.regressionautoml = autosklearn.regression.AutoSklearnRegressor(
time_left_for_this_task=300,
n_jobs=-1
)
automl.fit(
X_train_transformed,
df_train["label"]
)
predictions = automl.predict(X_train_transformed)
r2_score(df_train["label"], predictions)
>> 0.999
predictions = automl.predict(X_test_transformed)
r2_score(df_test["label"], predictions)
>> 0.999
PipelineProfiler output

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