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Regression

Model Dataset MAE RMSE R2 Memory in Mb Time in s
Adaptive Model Rules ChickWeights 24.1943 37.2166 0.725319 0.046977 5.25855
Adaptive Model Rules TrumpApproval 1.39847 2.43336 -1.02372 0.114429 9.38293
Adaptive Random Forest ChickWeights 26.1016 40.8094 0.669725 1.19043 56.006
Adaptive Random Forest TrumpApproval 0.800378 2.11495 -0.528761 1.28462 87.4457
Aggregated Mondrian Forest ChickWeights 25.6742 41.7123 0.65479 8.21412 127.415
Aggregated Mondrian Forest TrumpApproval 0.268533 0.349421 0.958184 16.9323 186.034
Bagging ChickWeights 23.1143 36.6311 0.733893 0.628034 38.0203
Bagging TrumpApproval 0.908203 2.23718 -0.710572 1.31579 82.0689
Exponentially Weighted Average ChickWeights 121.818 141.004 -2.94294 3.09241 55.8851
Exponentially Weighted Average TrumpApproval 40.7546 40.7905 -567.663 5.27613 141.452
Hoeffding Adaptive Tree ChickWeights 23.3739 37.6579 0.718766 0.0947332 7.99029
Hoeffding Adaptive Tree TrumpApproval 0.921313 2.23942 -0.713986 0.138225 16.7576
Hoeffding Tree ChickWeights 23.1619 36.7336 0.732402 0.0440512 6.29305
Hoeffding Tree TrumpApproval 0.956103 2.24987 -0.730022 0.148639 11.7656
Linear Regression ChickWeights 23.7587 37.0377 0.727954 0.00421047 3.21471
Linear Regression TrumpApproval 1.31455 3.91198 -4.23035 0.00497341 11.5379
Linear Regression with l1 regularization ChickWeights 23.7577 37.078 0.727361 0.00444126 9.7485
Linear Regression with l1 regularization TrumpApproval 1.15377 3.82872 -4.01007 0.0052042 13.3595
Linear Regression with l2 regularization ChickWeights 25.2738 38.5885 0.704694 0.00423336 1.22128
Linear Regression with l2 regularization TrumpApproval 1.87151 4.13052 -4.83107 0.0049963 4.15677
Passive-Aggressive Regressor, mode 1 ChickWeights 24.3423 37.596 0.71969 0.00345898 1.10187
Passive-Aggressive Regressor, mode 1 TrumpApproval 4.98403 6.97667 -15.6354 0.00443554 2.99338
Passive-Aggressive Regressor, mode 2 ChickWeights 100.624 143.066 -3.05911 0.00345898 1.16798
Passive-Aggressive Regressor, mode 2 TrumpApproval 31.0933 34.6257 -408.765 0.00443554 4.72475
River MLP ChickWeights 51.4078 80.9203 -0.298584 0.0123129 28.2295
River MLP TrumpApproval 1.58058 5.03392 -7.66066 0.0133505 32.2432
Stochastic Gradient Tree ChickWeights 68.7588 80.358 -0.280601 1.12059 22.3803
Stochastic Gradient Tree TrumpApproval 9.42975 17.9379 -108.972 3.08244 52.4507
Streaming Random Patches ChickWeights 23.7097 38.4416 0.706938 0.355182 93.4014
Streaming Random Patches TrumpApproval 0.656697 1.98434 -0.345761 1.06461 134.903
[baseline] Mean predictor ChickWeights 50.2509 71.1144 -0.00292947 0.000490189 0.302835
[baseline] Mean predictor TrumpApproval 1.56755 2.20286 -0.658483 0.000490189 1.08177
k-Nearest Neighbors ChickWeights 24.8406 39.2016 0.695236 2.88522 40.0878
k-Nearest Neighbors TrumpApproval 0.641679 1.59417 0.131425 5.03263 123.301

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020406080100120140160180200220240260280300320340360380400420440460480500520540560580600Instance020406080100120MAE020406080100120140160180200220240260280300320340360380400420440460480500520540560580600Instance050100150RMSE020406080100120140160180200220240260280300320340360380400420440460480500520540560580600Instance−4,000−3,000−2,000−1,0000R2020406080100120140160180200220240260280300320340360380400420440460480500520540560580600Instance02468Memory in Mb020406080100120140160180200220240260280300320340360380400420440460480500520540560580600Instance020406080100120Time in sAdaptive Model RulesAdaptive Random ForestAggregated Mondrian ForestBaggingExponentially Weighted AverageHoeffding Adaptive TreeHoeffding TreeLinear RegressionLinear Regression with l1 regularizationLinear Regression with l2 regularizationPassive-Aggressive Regressor, mode 1Passive-Aggressive Regressor, mode 2River MLPStochastic Gradient TreeStreaming Random Patches[baseline] Mean predictork-Nearest NeighborsModels

Datasets

ChickWeights

Chick weights along time.

The stream contains 578 items and 3 features. The goal is to predict the weight of each chick along time, according to the diet the chick is on. The data is ordered by time and then by chick.

Name  ChickWeights                                                                                                  
Task  Regression

Samples 578
Features 3
Sparse False
Path /Users/mastelini/miniconda3/envs/river-benchmark/lib/python3.10/site-packages/river/datasets/chick-weights.csv

TrumpApproval

Donald Trump approval ratings.

This dataset was obtained by reshaping the data used by FiveThirtyEight for analyzing Donald Trump's approval ratings. It contains 5 features, which are approval ratings collected by 5 polling agencies. The target is the approval rating from FiveThirtyEight's model. The goal of this task is to see if we can reproduce FiveThirtyEight's model.

Name  TrumpApproval                                                                                                     
Task  Regression

Samples 1,001
Features 6
Sparse False
Path /Users/mastelini/miniconda3/envs/river-benchmark/lib/python3.10/site-packages/river/datasets/trump_approval.csv.gz

Models

Linear Regression 

Pipeline (
  StandardScaler (
    with_std=True
  ),
  LinearRegression (
    optimizer=SGD (
      lr=Constant (
        learning_rate=0.01
      )
    )
    loss=Squared ()
    l2=0.
    l1=0.
    intercept_init=0.
    intercept_lr=Constant (
      learning_rate=0.01
    )
    clip_gradient=1e+12
    initializer=Zeros ()
  )
)

Linear Regression with l1 regularization 

Pipeline (
  StandardScaler (
    with_std=True
  ),
  LinearRegression (
    optimizer=SGD (
      lr=Constant (
        learning_rate=0.01
      )
    )
    loss=Squared ()
    l2=0.
    l1=1.
    intercept_init=0.
    intercept_lr=Constant (
      learning_rate=0.01
    )
    clip_gradient=1e+12
    initializer=Zeros ()
  )
)

Linear Regression with l2 regularization 

Pipeline (
  StandardScaler (
    with_std=True
  ),
  LinearRegression (
    optimizer=SGD (
      lr=Constant (
        learning_rate=0.01
      )
    )
    loss=Squared ()
    l2=1.
    l1=0.
    intercept_init=0.
    intercept_lr=Constant (
      learning_rate=0.01
    )
    clip_gradient=1e+12
    initializer=Zeros ()
  )
)

Passive-Aggressive Regressor, mode 1 

Pipeline (
  StandardScaler (
    with_std=True
  ),
  PARegressor (
    C=1.
    mode=1
    eps=0.1
    learn_intercept=True
  )
)

Passive-Aggressive Regressor, mode 2 

Pipeline (
  StandardScaler (
    with_std=True
  ),
  PARegressor (
    C=1.
    mode=2
    eps=0.1
    learn_intercept=True
  )
)

k-Nearest Neighbors 

Pipeline (
  StandardScaler (
    with_std=True
  ),
  KNNRegressor (
    n_neighbors=5
    engine=SWINN (
      graph_k=20
      dist_func=FunctionWrapper (
        distance_function=functools.partial(, p=2)
      )
      maxlen=1000
      warm_up=500
      max_candidates=50
      delta=0.0001
      prune_prob=0.
      n_iters=10
      seed=None
    )
    aggregation_method="mean"
  )
)

Hoeffding Tree 

Pipeline (
  StandardScaler (
    with_std=True
  ),
  HoeffdingTreeRegressor (
    grace_period=200
    max_depth=inf
    delta=1e-07
    tau=0.05
    leaf_prediction="adaptive"
    leaf_model=LinearRegression (
      optimizer=SGD (
        lr=Constant (
          learning_rate=0.01
        )
      )
      loss=Squared ()
      l2=0.
      l1=0.
      intercept_init=0.
      intercept_lr=Constant (
        learning_rate=0.01
      )
      clip_gradient=1e+12
      initializer=Zeros ()
    )
    model_selector_decay=0.95
    nominal_attributes=None
    splitter=TEBSTSplitter (
      digits=1
    )
    min_samples_split=5
    binary_split=False
    max_size=500.
    memory_estimate_period=1000000
    stop_mem_management=False
    remove_poor_attrs=False
    merit_preprune=True
  )
)

Hoeffding Adaptive Tree 

Pipeline (
  StandardScaler (
    with_std=True
  ),
  HoeffdingAdaptiveTreeRegressor (
    grace_period=200
    max_depth=inf
    delta=1e-07
    tau=0.05
    leaf_prediction="adaptive"
    leaf_model=LinearRegression (
      optimizer=SGD (
        lr=Constant (
          learning_rate=0.01
        )
      )
      loss=Squared ()
      l2=0.
      l1=0.
      intercept_init=0.
      intercept_lr=Constant (
        learning_rate=0.01
      )
      clip_gradient=1e+12
      initializer=Zeros ()
    )
    model_selector_decay=0.95
    nominal_attributes=None
    splitter=TEBSTSplitter (
      digits=1
    )
    min_samples_split=5
    bootstrap_sampling=True
    drift_window_threshold=300
    drift_detector=ADWIN (
      delta=0.002
      clock=32
      max_buckets=5
      min_window_length=5
      grace_period=10
    )
    switch_significance=0.05
    binary_split=False
    max_size=500.
    memory_estimate_period=1000000
    stop_mem_management=False
    remove_poor_attrs=False
    merit_preprune=True
    seed=42
  )
)

Stochastic Gradient Tree 

SGTRegressor (
  delta=1e-07
  grace_period=200
  init_pred=0.
  max_depth=inf
  lambda_value=0.1
  gamma=1.
  nominal_attributes=[]
  feature_quantizer=StaticQuantizer (
    n_bins=64
    warm_start=100
    buckets=None
  )
)

Adaptive Random Forest 

Pipeline (
  StandardScaler (
    with_std=True
  ),
  []
)

Aggregated Mondrian Forest 

[]

Adaptive Model Rules 

Pipeline (
  StandardScaler (
    with_std=True
  ),
  AMRules (
    n_min=200
    delta=1e-07
    tau=0.05
    pred_type="adaptive"
    pred_model=LinearRegression (
      optimizer=SGD (
        lr=Constant (
          learning_rate=0.01
        )
      )
      loss=Squared ()
      l2=0.
      l1=0.
      intercept_init=0.
      intercept_lr=Constant (
        learning_rate=0.01
      )
      clip_gradient=1e+12
      initializer=Zeros ()
    )
    splitter=TEBSTSplitter (
      digits=1
    )
    drift_detector=ADWIN (
      delta=0.002
      clock=32
      max_buckets=5
      min_window_length=5
      grace_period=10
    )
    fading_factor=0.99
    anomaly_threshold=-0.75
    m_min=30
    ordered_rule_set=True
    min_samples_split=5
  )
)

Streaming Random Patches 

Pipeline (
  StandardScaler (
    with_std=True
  ),
  SRPRegressor (
    model=HoeffdingTreeRegressor (
      grace_period=50
      max_depth=inf
      delta=0.01
      tau=0.05
      leaf_prediction="adaptive"
      leaf_model=LinearRegression (
        optimizer=SGD (
          lr=Constant (
            learning_rate=0.01
          )
        )
        loss=Squared ()
        l2=0.
        l1=0.
        intercept_init=0.
        intercept_lr=Constant (
          learning_rate=0.01
        )
        clip_gradient=1e+12
        initializer=Zeros ()
      )
      model_selector_decay=0.95
      nominal_attributes=None
      splitter=TEBSTSplitter (
        digits=1
      )
      min_samples_split=5
      binary_split=False
      max_size=500.
      memory_estimate_period=1000000
      stop_mem_management=False
      remove_poor_attrs=False
      merit_preprune=True
    )
    n_models=10
    subspace_size=0.6
    training_method="patches"
    lam=6
    drift_detector=ADWIN (
      delta=1e-05
      clock=32
      max_buckets=5
      min_window_length=5
      grace_period=10
    )
    warning_detector=ADWIN (
      delta=0.0001
      clock=32
      max_buckets=5
      min_window_length=5
      grace_period=10
    )
    disable_detector="off"
    disable_weighted_vote=True
    drift_detection_criteria="error"
    aggregation_method="mean"
    seed=42
    metric=MAE ()
  )
)

Bagging 

Pipeline (
  StandardScaler (
    with_std=True
  ),
  [HoeffdingAdaptiveTreeRegressor (
    grace_period=200
    max_depth=inf
    delta=1e-07
    tau=0.05
    leaf_prediction="adaptive"
    leaf_model=LinearRegression (
      optimizer=SGD (
        lr=Constant (
          learning_rate=0.01
        )
      )
      loss=Squared ()
      l2=0.
      l1=0.
      intercept_init=0.
      intercept_lr=Constant (
        learning_rate=0.01
      )
      clip_gradient=1e+12
      initializer=Zeros ()
    )
    model_selector_decay=0.95
    nominal_attributes=None
    splitter=TEBSTSplitter (
      digits=1
    )
    min_samples_split=5
    bootstrap_sampling=False
    drift_window_threshold=300
    drift_detector=ADWIN (
      delta=0.002
      clock=32
      max_buckets=5
      min_window_length=5
      grace_period=10
    )
    switch_significance=0.05
    binary_split=False
    max_size=500.
    memory_estimate_period=1000000
    stop_mem_management=False
    remove_poor_attrs=False
    merit_preprune=True
    seed=None
  ), HoeffdingAdaptiveTreeRegressor (
    grace_period=200
    max_depth=inf
    delta=1e-07
    tau=0.05
    leaf_prediction="adaptive"
    leaf_model=LinearRegression (
      optimizer=SGD (
        lr=Constant (
          learning_rate=0.01
        )
      )
      loss=Squared ()
      l2=0.
      l1=0.
      intercept_init=0.
      intercept_lr=Constant (
        learning_rate=0.01
      )
      clip_gradient=1e+12
      initializer=Zeros ()
    )
    model_selector_decay=0.95
    nominal_attributes=None
    splitter=TEBSTSplitter (
      digits=1
    )
    min_samples_split=5
    bootstrap_sampling=False
    drift_window_threshold=300
    drift_detector=ADWIN (
      delta=0.002
      clock=32
      max_buckets=5
      min_window_length=5
      grace_period=10
    )
    switch_significance=0.05
    binary_split=False
    max_size=500.
    memory_estimate_period=1000000
    stop_mem_management=False
    remove_poor_attrs=False
    merit_preprune=True
    seed=None
  ), HoeffdingAdaptiveTreeRegressor (
    grace_period=200
    max_depth=inf
    delta=1e-07
    tau=0.05
    leaf_prediction="adaptive"
    leaf_model=LinearRegression (
      optimizer=SGD (
        lr=Constant (
          learning_rate=0.01
        )
      )
      loss=Squared ()
      l2=0.
      l1=0.
      intercept_init=0.
      intercept_lr=Constant (
        learning_rate=0.01
      )
      clip_gradient=1e+12
      initializer=Zeros ()
    )
    model_selector_decay=0.95
    nominal_attributes=None
    splitter=TEBSTSplitter (
      digits=1
    )
    min_samples_split=5
    bootstrap_sampling=False
    drift_window_threshold=300
    drift_detector=ADWIN (
      delta=0.002
      clock=32
      max_buckets=5
      min_window_length=5
      grace_period=10
    )
    switch_significance=0.05
    binary_split=False
    max_size=500.
    memory_estimate_period=1000000
    stop_mem_management=False
    remove_poor_attrs=False
    merit_preprune=True
    seed=None
  ), HoeffdingAdaptiveTreeRegressor (
    grace_period=200
    max_depth=inf
    delta=1e-07
    tau=0.05
    leaf_prediction="adaptive"
    leaf_model=LinearRegression (
      optimizer=SGD (
        lr=Constant (
          learning_rate=0.01
        )
      )
      loss=Squared ()
      l2=0.
      l1=0.
      intercept_init=0.
      intercept_lr=Constant (
        learning_rate=0.01
      )
      clip_gradient=1e+12
      initializer=Zeros ()
    )
    model_selector_decay=0.95
    nominal_attributes=None
    splitter=TEBSTSplitter (
      digits=1
    )
    min_samples_split=5
    bootstrap_sampling=False
    drift_window_threshold=300
    drift_detector=ADWIN (
      delta=0.002
      clock=32
      max_buckets=5
      min_window_length=5
      grace_period=10
    )
    switch_significance=0.05
    binary_split=False
    max_size=500.
    memory_estimate_period=1000000
    stop_mem_management=False
    remove_poor_attrs=False
    merit_preprune=True
    seed=None
  ), HoeffdingAdaptiveTreeRegressor (
    grace_period=200
    max_depth=inf
    delta=1e-07
    tau=0.05
    leaf_prediction="adaptive"
    leaf_model=LinearRegression (
      optimizer=SGD (
        lr=Constant (
          learning_rate=0.01
        )
      )
      loss=Squared ()
      l2=0.
      l1=0.
      intercept_init=0.
      intercept_lr=Constant (
        learning_rate=0.01
      )
      clip_gradient=1e+12
      initializer=Zeros ()
    )
    model_selector_decay=0.95
    nominal_attributes=None
    splitter=TEBSTSplitter (
      digits=1
    )
    min_samples_split=5
    bootstrap_sampling=False
    drift_window_threshold=300
    drift_detector=ADWIN (
      delta=0.002
      clock=32
      max_buckets=5
      min_window_length=5
      grace_period=10
    )
    switch_significance=0.05
    binary_split=False
    max_size=500.
    memory_estimate_period=1000000
    stop_mem_management=False
    remove_poor_attrs=False
    merit_preprune=True
    seed=None
  ), HoeffdingAdaptiveTreeRegressor (
    grace_period=200
    max_depth=inf
    delta=1e-07
    tau=0.05
    leaf_prediction="adaptive"
    leaf_model=LinearRegression (
      optimizer=SGD (
        lr=Constant (
          learning_rate=0.01
        )
      )
      loss=Squared ()
      l2=0.
      l1=0.
      intercept_init=0.
      intercept_lr=Constant (
        learning_rate=0.01
      )
      clip_gradient=1e+12
      initializer=Zeros ()
    )
    model_selector_decay=0.95
    nominal_attributes=None
    splitter=TEBSTSplitter (
      digits=1
    )
    min_samples_split=5
    bootstrap_sampling=False
    drift_window_threshold=300
    drift_detector=ADWIN (
      delta=0.002
      clock=32
      max_buckets=5
      min_window_length=5
      grace_period=10
    )
    switch_significance=0.05
    binary_split=False
    max_size=500.
    memory_estimate_period=1000000
    stop_mem_management=False
    remove_poor_attrs=False
    merit_preprune=True
    seed=None
  ), HoeffdingAdaptiveTreeRegressor (
    grace_period=200
    max_depth=inf
    delta=1e-07
    tau=0.05
    leaf_prediction="adaptive"
    leaf_model=LinearRegression (
      optimizer=SGD (
        lr=Constant (
          learning_rate=0.01
        )
      )
      loss=Squared ()
      l2=0.
      l1=0.
      intercept_init=0.
      intercept_lr=Constant (
        learning_rate=0.01
      )
      clip_gradient=1e+12
      initializer=Zeros ()
    )
    model_selector_decay=0.95
    nominal_attributes=None
    splitter=TEBSTSplitter (
      digits=1
    )
    min_samples_split=5
    bootstrap_sampling=False
    drift_window_threshold=300
    drift_detector=ADWIN (
      delta=0.002
      clock=32
      max_buckets=5
      min_window_length=5
      grace_period=10
    )
    switch_significance=0.05
    binary_split=False
    max_size=500.
    memory_estimate_period=1000000
    stop_mem_management=False
    remove_poor_attrs=False
    merit_preprune=True
    seed=None
  ), HoeffdingAdaptiveTreeRegressor (
    grace_period=200
    max_depth=inf
    delta=1e-07
    tau=0.05
    leaf_prediction="adaptive"
    leaf_model=LinearRegression (
      optimizer=SGD (
        lr=Constant (
          learning_rate=0.01
        )
      )
      loss=Squared ()
      l2=0.
      l1=0.
      intercept_init=0.
      intercept_lr=Constant (
        learning_rate=0.01
      )
      clip_gradient=1e+12
      initializer=Zeros ()
    )
    model_selector_decay=0.95
    nominal_attributes=None
    splitter=TEBSTSplitter (
      digits=1
    )
    min_samples_split=5
    bootstrap_sampling=False
    drift_window_threshold=300
    drift_detector=ADWIN (
      delta=0.002
      clock=32
      max_buckets=5
      min_window_length=5
      grace_period=10
    )
    switch_significance=0.05
    binary_split=False
    max_size=500.
    memory_estimate_period=1000000
    stop_mem_management=False
    remove_poor_attrs=False
    merit_preprune=True
    seed=None
  ), HoeffdingAdaptiveTreeRegressor (
    grace_period=200
    max_depth=inf
    delta=1e-07
    tau=0.05
    leaf_prediction="adaptive"
    leaf_model=LinearRegression (
      optimizer=SGD (
        lr=Constant (
          learning_rate=0.01
        )
      )
      loss=Squared ()
      l2=0.
      l1=0.
      intercept_init=0.
      intercept_lr=Constant (
        learning_rate=0.01
      )
      clip_gradient=1e+12
      initializer=Zeros ()
    )
    model_selector_decay=0.95
    nominal_attributes=None
    splitter=TEBSTSplitter (
      digits=1
    )
    min_samples_split=5
    bootstrap_sampling=False
    drift_window_threshold=300
    drift_detector=ADWIN (
      delta=0.002
      clock=32
      max_buckets=5
      min_window_length=5
      grace_period=10
    )
    switch_significance=0.05
    binary_split=False
    max_size=500.
    memory_estimate_period=1000000
    stop_mem_management=False
    remove_poor_attrs=False
    merit_preprune=True
    seed=None
  ), HoeffdingAdaptiveTreeRegressor (
    grace_period=200
    max_depth=inf
    delta=1e-07
    tau=0.05
    leaf_prediction="adaptive"
    leaf_model=LinearRegression (
      optimizer=SGD (
        lr=Constant (
          learning_rate=0.01
        )
      )
      loss=Squared ()
      l2=0.
      l1=0.
      intercept_init=0.
      intercept_lr=Constant (
        learning_rate=0.01
      )
      clip_gradient=1e+12
      initializer=Zeros ()
    )
    model_selector_decay=0.95
    nominal_attributes=None
    splitter=TEBSTSplitter (
      digits=1
    )
    min_samples_split=5
    bootstrap_sampling=False
    drift_window_threshold=300
    drift_detector=ADWIN (
      delta=0.002
      clock=32
      max_buckets=5
      min_window_length=5
      grace_period=10
    )
    switch_significance=0.05
    binary_split=False
    max_size=500.
    memory_estimate_period=1000000
    stop_mem_management=False
    remove_poor_attrs=False
    merit_preprune=True
    seed=None
  )]
)

Exponentially Weighted Average 

Pipeline (
  StandardScaler (
    with_std=True
  ),
  [LinearRegression (
    optimizer=SGD (
      lr=Constant (
        learning_rate=0.01
      )
    )
    loss=Squared ()
    l2=0.
    l1=0.
    intercept_init=0.
    intercept_lr=Constant (
      learning_rate=0.01
    )
    clip_gradient=1e+12
    initializer=Zeros ()
  ), HoeffdingAdaptiveTreeRegressor (
    grace_period=200
    max_depth=inf
    delta=1e-07
    tau=0.05
    leaf_prediction="adaptive"
    leaf_model=LinearRegression (
      optimizer=SGD (
        lr=Constant (
          learning_rate=0.01
        )
      )
      loss=Squared ()
      l2=0.
      l1=0.
      intercept_init=0.
      intercept_lr=Constant (
        learning_rate=0.01
      )
      clip_gradient=1e+12
      initializer=Zeros ()
    )
    model_selector_decay=0.95
    nominal_attributes=None
    splitter=TEBSTSplitter (
      digits=1
    )
    min_samples_split=5
    bootstrap_sampling=True
    drift_window_threshold=300
    drift_detector=ADWIN (
      delta=0.002
      clock=32
      max_buckets=5
      min_window_length=5
      grace_period=10
    )
    switch_significance=0.05
    binary_split=False
    max_size=500.
    memory_estimate_period=1000000
    stop_mem_management=False
    remove_poor_attrs=False
    merit_preprune=True
    seed=None
  ), KNNRegressor (
    n_neighbors=5
    engine=SWINN (
      graph_k=20
      dist_func=FunctionWrapper (
        distance_function=functools.partial(, p=2)
      )
      maxlen=1000
      warm_up=500
      max_candidates=50
      delta=0.0001
      prune_prob=0.
      n_iters=10
      seed=None
    )
    aggregation_method="mean"
  ), AMRules (
    n_min=200
    delta=1e-07
    tau=0.05
    pred_type="adaptive"
    pred_model=LinearRegression (
      optimizer=SGD (
        lr=Constant (
          learning_rate=0.01
        )
      )
      loss=Squared ()
      l2=0.
      l1=0.
      intercept_init=0.
      intercept_lr=Constant (
        learning_rate=0.01
      )
      clip_gradient=1e+12
      initializer=Zeros ()
    )
    splitter=TEBSTSplitter (
      digits=1
    )
    drift_detector=ADWIN (
      delta=0.002
      clock=32
      max_buckets=5
      min_window_length=5
      grace_period=10
    )
    fading_factor=0.99
    anomaly_threshold=-0.75
    m_min=30
    ordered_rule_set=True
    min_samples_split=5
  )]
)

River MLP 

Pipeline (
  StandardScaler (
    with_std=True
  ),
  MLPRegressor (
    hidden_dims=(5,)
    activations=(, , )
    loss=Squared ()
    optimizer=SGD (
      lr=Constant (
        learning_rate=0.001
      )
    )
    seed=42
  )
)

[baseline] Mean predictor 

StatisticRegressor (
  statistic=Mean ()
)

Environment

Python implementation: CPython
Python version       : 3.12.9
IPython version      : 8.29.0

river       : 0.22.0
numpy       : 2.0.2
scikit-learn: 1.5.1
pandas      : 2.2.3
scipy       : 1.14.1

Compiler    : GCC 13.3.0
OS          : Linux
Release     : 6.8.0-1021-azure
Machine     : x86_64
Processor   : x86_64
CPU cores   : 4
Architecture: 64bit