sklearn Boston Dataset-based System Evaluation Task

A sklearn Boston Dataset-based System Evaluation Task is a supervised numeric-value prediction system evaluation task (for supervised numeric-value prediction systems) based on sklearn's Boston dataset.

• Context:
  • It can be an sklearn Boston Dataset-based Regression Trees Evaluation.
  • It can be an sklearn Boston Dataset-based SVM Regression Evaluation.
  • It can be an sklearn Boston Dataset-based Neural Networks Regression Evaluation.
• Example(s):
  • 10-fold RMSE on the data, source code:
    • ~4.65 for sklearn.ensemble.GradientBoostingRegressor Gradient Boosting Regressor.
    • ~4.67 for sklearn.ensemble.BaggingRegressor, a Bagging regressor.
    • ~4.82 for sklearn.ensemble.ExtraTreesRegressor, an Ensemble Extra Trees Regressor.
    • ~4.88 for sklearn.ensemble.RandomForestRegressor, a Random Forest Regressor.
    • ~5.05 for sklearn.ensemble.AdaBoostRegressor, an AdaBoost regressor.
    • ~5.58 for sklearn.linear_model.ElasticNet(), an ElasticNet regression system.
    • ~5.74 for sklearn.linear model.Lasso[1], a LASSO regression system.
    • ~5.84 for sklearn.linear_model.Ridge(), a Ridge regression system.
    • ~5.85 for sklearn.linear_model.BayesianRidge(), a Bayesian Ridge regression system.
    • ~5.88 for sklearn.linear_model.LinearRegression(), a Linear regression system.
    • ~5.91 for sklearn.linear_model.ARDRegression(), a ARD regression system.
    • ~6.00 for sklearn.tree.DecisionTreeRegressor, a simple Regression Tree Learning System.
    • ~6.29 for sklearn.linear_model.Lars(), a Least Angle regression system.
    • ~6.49 for sklearn.linear_model.HuberRegressor(), a Huber regression system.
    • ~6.79 for sklearn.neural_network.MLPRegressor(activation='relu', solver='lbfgs').
    • ~8.59 for sklearn.svm.LinearSVR.
  • …
• Counter-Example(s):
  • sklearn.linear_model.LinearRegression() [2]], a Linear Regression System.
  • sklearn.linear_model.Ridge() [3], a Ridge Regression System.
  • sklearn.linear_model.ARDRegression(), a ARD Regression System.
  • sklearn.linear_model.BayesianRidge(), a Bayesian Ridge Regression System.
  • sklearn.linear_model.ElasticNet(), an ElasticNet_System.
  • sklearn.linear_model.ElasticNetCV(), an ElasticNet Cross-Validation System.
  • sklearn.linear_model.HuberRegressor(), a Huber Regression System.
  • sklearn.linear_model.Lars(), a Least Angle Regression System.
  • sklearn.linear_model.LarsCV(), a Least Angle Regression Cross-Validation System.
• See: sklearn.linear model, scikit-learn, Python, Scipy Numpy, TensorFlow, Pytorch.

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