Naval Propulsion (UCI, Regression, n=11934, d=16)

Loading The Data

from kxy_datasets.uci_regressions import NavalPropulsion # pip install kxy_datasets

dataset = NavalPropulsion()
df = dataset.df # Retrieve the dataset as a pandas dataframe
y_column = dataset.y_column # The name of the column corresponding to the target
problem_type = dataset.problem_type # 'regression' or 'classification'

df.kxy.describe() # Visualize a summary of the data

-----------
Column: GGn
-----------
Type:   Continuous
Max:    9,797
p75:    9,132
Mean:   8,200
Median: 8,482
p25:    7,058
Min:    6,589

---------------------------
Column: GT Compressor Decay
---------------------------
Type:   Continuous
Max:    1.0
p75:    1.0
Mean:   1.0
Median: 1.0
p25:    1.0
Min:    0.9

------------------------
Column: GT Turbine Decay
------------------------
Type:   Continuous
Max:    1.0
p75:    1.0
Mean:   1.0
Median: 1.0
p25:    1.0
Min:    1.0

-----------
Column: GTT
-----------
Type:   Continuous
Max:    72,784
p75:    39,001
Mean:   27,247
Median: 21,630
p25:    8,375
Min:    253

-----------
Column: GTn
-----------
Type:   Continuous
Max:    3,560
p75:    2,678
Mean:   2,136
Median: 1,924
p25:    1,386
Min:    1,307

----------
Column: LP
----------
Type:   Continuous
Max:    9.3
p75:    7.1
Mean:   5.2
Median: 5.1
p25:    3.1
Min:    1.1

----------
Column: P1
----------
Type:   Continuous
Max:    1.0
p75:    1.0
Mean:   1.0
Median: 1.0
p25:    1.0
Min:    1.0

----------
Column: P2
----------
Type:   Continuous
Max:    23
p75:    15
Mean:   12
Median: 11
p25:    7.4
Min:    5.8

-----------
Column: P48
-----------
Type:   Continuous
Max:    4.6
p75:    3.0
Mean:   2.4
Median: 2.1
p25:    1.4
Min:    1.1

------------
Column: Pexh
------------
Type:   Continuous
Max:    1.1
p75:    1.0
Mean:   1.0
Median: 1.0
p25:    1.0
Min:    1.0

----------
Column: T1
----------
Type:   Continuous
Max:    288
p75:    288
Mean:   288
Median: 288
p25:    288
Min:    288

----------
Column: T2
----------
Type:   Continuous
Max:    789
p75:    693
Mean:   646
Median: 637
p25:    578
Min:    540

-----------
Column: T48
-----------
Type:   Continuous
Max:    1,115
p75:    834
Mean:   735
Median: 706
p25:    589
Min:    442

-----------
Column: TIC
-----------
Type:   Continuous
Max:    92
p75:    44
Mean:   33
Median: 25
p25:    13
Min:    0.0

----------
Column: Tp
----------
Type:   Continuous
Max:    645
p75:    332
Mean:   227
Median: 175
p25:    60
Min:    5.3

----------
Column: Ts
----------
Type:   Continuous
Max:    645
p75:    332
Mean:   227
Median: 175
p25:    60
Min:    5.3

---------
Column: V
---------
Type:   Continuous
Max:    27
p75:    21
Mean:   15
Median: 15
p25:    9.0
Min:    3.0

----------
Column: mf
----------
Type:   Continuous
Max:    1.8
p75:    0.9
Mean:   0.7
Median: 0.5
p25:    0.2
Min:    0.1

Data Valuation

df.kxy.data_valuation(y_column, problem_type=problem_type)

[====================================================================================================] 100% ETA: 0s    Duration: 0s

	Achievable R-Squared	Achievable Log-Likelihood Per Sample	Achievable RMSE
0	0.01	-4.42	1.47e-02

Automatic (Model-Free) Variable Selection

df.kxy.variable_selection(y_column, problem_type=problem_type)

[====================================================================================================] 100% ETA: 0s    Duration: 0s

	Variable	Running Achievable R-Squared	Running Achievable RMSE
Selection Order
0	No Variable	0.00	0.0147
1	GGn	0.01	0.0147
2	P48	0.01	0.0147
3	T2	0.01	0.0147
4	GTT	0.01	0.0147
5	T48	0.01	0.0147
6	LP	0.01	0.0147
7	Tp	0.01	0.0147
8	GT Turbine Decay	0.01	0.0147
9	Ts	0.01	0.0147
10	V	0.01	0.0147
11	Pexh	0.01	0.0147
12	TIC	0.01	0.0147
13	GTn	0.01	0.0147
14	mf	0.01	0.0147
15	P2	0.01	0.0147