Regression

TableChart

Model	Dataset	MAE	RMSE	R2	Memory in Mb	Time in s
Adaptive Model Rules	ChickWeights	24.0925	37.1369	0.719675	0.0469542	5.03028
Adaptive Model Rules	TrumpApproval	1.40204	2.43644	-1.02749	0.114429	5.76779
Adaptive Random Forest	ChickWeights	25.9648	40.6034	0.6649	1.18613	32.8286
Adaptive Random Forest	TrumpApproval	0.801133	2.11603	-0.529292	1.28362	54.6942
Bagging	ChickWeights	23.0595	36.5862	0.727928	0.643575	19.8658
Bagging	TrumpApproval	0.904415	2.23483	-0.705833	1.33501	42.6904
Exponentially Weighted Average	ChickWeights	120.54	139.462	-2.95334	0.183387	12.3806
Exponentially Weighted Average	TrumpApproval	40.7536	40.7895	-567.257	0.316642	30.0432
Hoeffding Adaptive Tree	ChickWeights	23.2557	37.579	0.712962	0.0946112	5.75782
Hoeffding Adaptive Tree	TrumpApproval	0.910675	2.2343	-0.705019	0.138225	6.69917
Hoeffding Tree	ChickWeights	23.0842	36.6638	0.726773	0.0440512	4.02236
Hoeffding Tree	TrumpApproval	0.949745	2.24815	-0.726224	0.148639	9.13796
Linear Regression	ChickWeights	23.8353	37.0287	0.721307	0.00421047	2.10647
Linear Regression	TrumpApproval	1.3486	4.12828	-4.82084	0.00497341	3.6327
Linear Regression with l1 regularization	ChickWeights	23.868	37.0773	0.720575	0.00444126	1.13401
Linear Regression with l1 regularization	TrumpApproval	1.21585	4.06821	-4.65269	0.0052042	2.06156
Linear Regression with l2 regularization	ChickWeights	25.5204	38.6553	0.696284	0.00423336	1.11618
Linear Regression with l2 regularization	TrumpApproval	1.99918	4.40997	-5.64232	0.0049963	1.98704
Passive-Aggressive Regressor, mode 1	ChickWeights	24.2339	37.5576	0.713289	0.00345898	1.33977
Passive-Aggressive Regressor, mode 1	TrumpApproval	4.90639	6.6656	-14.1749	0.00443554	2.18425
Passive-Aggressive Regressor, mode 2	ChickWeights	99.5681	141.4	-3.06396	0.00345898	1.99155
Passive-Aggressive Regressor, mode 2	TrumpApproval	31.1288	34.4257	-403.774	0.00443554	2.19594
River MLP	ChickWeights	49.5783	77.9026	-0.233541	0.0123129	18.4913
River MLP	TrumpApproval	1.59139	5.147	-8.04808	0.0133505	30.7873
Stochastic Gradient Tree	ChickWeights	68.1198	79.5649	-0.286746	1.12059	9.48214
Stochastic Gradient Tree	TrumpApproval	9.43874	17.9468	-109.008	3.08244	24.6638
Streaming Random Patches	ChickWeights	23.5162	38.2072	0.703285	0.558536	50.7829
Streaming Random Patches	TrumpApproval	0.640561	1.97134	-0.32731	1.05934	101.873
[baseline] Mean predictor	ChickWeights	49.4914	70.2457	-0.00297194	0.000490189	0.529127
[baseline] Mean predictor	TrumpApproval	1.56814	2.20374	-0.658701	0.000490189	0.8379
k-Nearest Neighbors	ChickWeights	22.9043	34.7945	0.753924	0.0461216	4.35991
k-Nearest Neighbors	TrumpApproval	0.493975	1.50807	0.223232	0.0660038	9.48546

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-224.6021011118197, "Memory in Mb": 0.0004901885986328, "Time in s": 0.003379 }, { "step": 40, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 2.3994713447037435, "RMSE": 7.102066178895935, "R2": -19.27845129783118, "Memory in Mb": 0.0004901885986328, "Time in s": 0.0081789999999999 }, { "step": 60, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.8170744682035584, "RMSE": 5.815253847056423, "R2": -17.329373299766118, "Memory in Mb": 0.0004901885986328, "Time in s": 0.0135909999999999 }, { "step": 80, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.604995404573344, "RMSE": 5.081770494168446, "R2": -13.040545957103586, "Memory in Mb": 0.0004901885986328, "Time in s": 0.0195849999999999 }, { "step": 100, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.824259078948539, "RMSE": 4.70488333223354, 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"R2": -2.4756944369169624, "Memory in Mb": 0.0004901885986328, "Time in s": 0.0586379999999999 }, { "step": 200, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 2.051125997923389, "RMSE": 3.731810291394655, "R2": -2.23527456693896, "Memory in Mb": 0.0004901885986328, "Time in s": 0.0682329999999999 }, { "step": 220, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.94095193468414, "RMSE": 3.56902990398404, "R2": -2.19210047340805, "Memory in Mb": 0.0004901885986328, "Time in s": 0.078397 }, { "step": 240, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.9366756524315063, "RMSE": 3.4612902974772624, "R2": -2.024876884626847, "Memory in Mb": 0.0004901885986328, "Time in s": 0.089124 }, { "step": 260, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.9250039777458068, "RMSE": 3.363327951159923, "R2": -1.8945640461454525, "Memory in Mb": 0.0004901885986328, "Time in s": 0.1004139999999999 }, { "step": 280, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.8726934920539136, "RMSE": 3.257010428159885, "R2": -1.8420037280027224, "Memory in Mb": 0.0004901885986328, "Time in s": 0.11227 }, { "step": 300, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.8907476896224935, "RMSE": 3.1958821895815714, "R2": -1.6910252267675163, "Memory in Mb": 0.0004901885986328, "Time in s": 0.124747 }, { "step": 320, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.819623890420079, "RMSE": 3.103812605138666, "R2": -1.663886258690169, "Memory in Mb": 0.0004901885986328, "Time in s": 0.137792 }, { "step": 340, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.7396293145937214, "RMSE": 3.014220627768389, "R2": -1.654906383755708, "Memory in Mb": 0.0004901885986328, "Time in s": 0.151284 }, { "step": 360, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.7350691203787965, "RMSE": 2.9569384317632506, "R2": -1.5759385016835008, "Memory in Mb": 0.0004901885986328, "Time in s": 0.165202 }, { "step": 380, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.6987131960417108, "RMSE": 2.8893997308323693, "R2": -1.5446951110541192, "Memory in Mb": 0.0004901885986328, "Time in s": 0.179547 }, { "step": 400, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.673610627740774, "RMSE": 2.82935583501861, "R2": -1.5089937655143242, "Memory in Mb": 0.0004901885986328, "Time in s": 0.194363 }, { "step": 420, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.6410137122925974, "RMSE": 2.7701802079251965, "R2": -1.484737486096575, "Memory in Mb": 0.0004901885986328, "Time in s": 0.209602 }, { "step": 440, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.6565972573555454, "RMSE": 2.7427790467379385, "R2": -1.391750010744973, "Memory in Mb": 0.0004901885986328, "Time in s": 0.225268 }, { "step": 460, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.699464840115161, "RMSE": 2.73946740401384, "R2": -1.2626191030939884, "Memory in Mb": 0.0004901885986328, "Time in s": 0.241356 }, { "step": 480, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.7224824441896145, "RMSE": 2.7219018737730583, "R2": -1.182307732575659, "Memory in Mb": 0.0004901885986328, "Time in s": 0.257864 }, { "step": 500, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.7446092142173422, "RMSE": 2.70580354422956, "R2": -1.1113262021905803, "Memory in Mb": 0.0004901885986328, "Time in s": 0.274789 }, { "step": 520, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.7464998751860934, "RMSE": 2.677192702589883, "R2": -1.0705208906620065, "Memory in Mb": 0.0004901885986328, "Time in s": 0.292197 }, { "step": 540, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.7535492786865423, "RMSE": 2.653885630983747, "R2": -1.027170706279252, "Memory in Mb": 0.0004901885986328, "Time in s": 0.310025 }, { "step": 560, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.7201019899937544, "RMSE": 2.614359234374483, "R2": -1.0141103337708768, "Memory in Mb": 0.0004901885986328, "Time in s": 0.328271 }, { "step": 580, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.6887559504032663, "RMSE": 2.5757257291728384, "R2": -1.0033760803823184, "Memory in Mb": 0.0004901885986328, "Time in s": 0.346933 }, { "step": 600, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.701917368353294, "RMSE": 2.561424763732869, "R2": -0.9592753712060648, "Memory in Mb": 0.0004901885986328, "Time in s": 0.366058 }, { "step": 620, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.7178157166185173, "RMSE": 2.551346895968156, "R2": -0.9142580419512064, "Memory in Mb": 0.0004901885986328, "Time in s": 0.38561 }, { "step": 640, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.7365901196485038, "RMSE": 2.545046385321895, "R2": -0.8692105635365064, "Memory in Mb": 0.0004901885986328, "Time in s": 0.405582 }, { "step": 660, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.7465677425181807, "RMSE": 2.532051562790666, "R2": -0.8368676529707118, "Memory in Mb": 0.0004901885986328, "Time in s": 0.425968 }, { "step": 680, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.731617734826669, "RMSE": 2.504226186170861, "R2": -0.8251107974736909, "Memory in Mb": 0.0004901885986328, "Time in s": 0.446783 }, { "step": 700, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.6973720107412231, "RMSE": 2.47026789197972, "R2": -0.8225927549994396, "Memory in Mb": 0.0004901885986328, "Time in s": 0.468073 }, { "step": 720, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.6698372433333928, "RMSE": 2.4400355004771077, "R2": -0.81732226470892, "Memory in Mb": 0.0004901885986328, "Time in s": 0.489788 }, { "step": 740, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.6732482399922957, "RMSE": 2.425592833263792, "R2": -0.7947920429290933, "Memory in Mb": 0.0004901885986328, "Time in s": 0.511921 }, { "step": 760, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.6653913599894004, "RMSE": 2.404136439714782, "R2": -0.7822814452716051, "Memory in Mb": 0.0004901885986328, "Time in s": 0.53447 }, { "step": 780, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.6644612180457288, "RMSE": 2.387561393188575, "R2": -0.7656652158374817, "Memory in Mb": 0.0004901885986328, "Time in s": 0.557436 }, { "step": 800, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.6556359332933146, "RMSE": 2.368497267913513, "R2": -0.7532954885990883, "Memory in Mb": 0.0004901885986328, "Time in s": 0.580851 }, { "step": 820, "track": "Regression", "model": "[baseline] Mean predictor", "dataset": "TrumpApproval", "MAE": 1.6452077788467467, "RMSE": 2.348678653798561, "R2": -0.7430103139622937, "Memory in Mb": 0.0004901885986328, "Time in s": 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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 /home/kulbach/projects/river/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 /home/kulbach/projects/river/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
    window_size=100
    aggregation_method="mean"
    min_distance_keep=0.
    distance_func=functools.partial(, p=2)
  )
)

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
  ),
  []
)

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
    window_size=100
    aggregation_method="mean"
    min_distance_keep=0.
    distance_func=functools.partial(, p=2)
  ), 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       : 3.9.17
IPython version      : 8.15.0

river       : 0.19.0
numpy       : 1.25.2
scikit-learn: 1.3.0
pandas      : 2.1.0
scipy       : 1.11.2

Compiler    : GCC 11.3.0
OS          : Linux
Release     : 5.15.0-1041-azure
Machine     : x86_64
Processor   : x86_64
CPU cores   : 2
Architecture: 64bit