#=============================================
# Regression by scikit-learn functions
#=============================================
infile =D:/tkProg/tkProg.main/tkprog_COE/regression/random-poly-ML.xlsx
outfile=D:\tkProg\tkProg.main\tkprog_COE\regression/random-poly-ML-predict.xlsx
method=linear
Fraction of test data=0.3
Seed of random() for split data=
nmaxiter=1000
For Ridge/LASSO/Elastic Net:
  alpha=0.1
  l1_ratio=0.5
For Random Forest Regression:
  max_depth=1000
  n_estimators=100
  max_features=auto
For Multilayer Perceptoron Regression:
  hidden layer sizes=5,5,5
  mlp_solver=lbfgs
  mlp_activation=relu
Plot options:
  plot_boxplot=0
  plot_heatmap=0
  plot_pairplot=0

Read [D:/tkProg/tkProg.main/tkprog_COE/regression/random-poly-ML.xlsx]

Data check
isnull()         y    x^0    x^1    x^2    x^3
0    False  False  False  False  False
1    False  False  False  False  False
2    False  False  False  False  False
3    False  False  False  False  False
4    False  False  False  False  False
..     ...    ...    ...    ...    ...
96   False  False  False  False  False
97   False  False  False  False  False
98   False  False  False  False  False
99   False  False  False  False  False
100  False  False  False  False  False

[101 rows x 5 columns]
isna()         y    x^0    x^1    x^2    x^3
0    False  False  False  False  False
1    False  False  False  False  False
2    False  False  False  False  False
3    False  False  False  False  False
4    False  False  False  False  False
..     ...    ...    ...    ...    ...
96   False  False  False  False  False
97   False  False  False  False  False
98   False  False  False  False  False
99   False  False  False  False  False
100  False  False  False  False  False

[101 rows x 5 columns]
ndata=101
ndescriptors=4
  all_labels=['y', 'x^0', 'x^1', 'x^2', 'x^3']
  x_labels  =['x^0', 'x^1', 'x^2', 'x^3']
  o_label   =y

Split to training and test data
 Number of training data:70
 Number of test data:31

Covariances of standardized values
  ( 0,  1) (         y,        x^0):   0.3066
  ( 0,  2) (         y,        x^1):   0.2906
  ( 0,  3) (         y,        x^2):   0.2826
  ( 0,  4) (         y,        x^3):   0.2839
  ( 1,  2) (       x^0,        x^1):   0.2865
  ( 1,  3) (       x^0,        x^2):   0.2799
  ( 1,  4) (       x^0,        x^3):   0.2780
  ( 2,  3) (       x^1,        x^2):   0.2777
  ( 2,  4) (       x^1,        x^3):   0.2718
  ( 3,  4) (       x^2,        x^3):   0.2666

Execute regression
Standaridization
Fit

Calculate predicted values

Scores:
  Mean absolute error (MAE): training       3.36  test:       3.64
  Mean squared error (MSE) : training       15.9  test:       18.7
  Root MSE (RMSE)          : training       3.99  test:       4.32
  R^2 score                : training      0.953  test:      0.945

Parameters:
  intercept: 22.437025191196067
  coefficients
         x^0:            0
         x^1:       -3.483
         x^2:        9.818
         x^3:        11.48

Save predict data to [D:\tkProg\tkProg.main\tkprog_COE\regression/random-poly-ML-predict.xlsx]

plot

Plot index - input/prediction

Plot input - prediction

Press ENTER to terminate

clicked for the data [train] in the input - predict plot (inf #4  data index #10)
  #  10: input: 22.15454056398367       predict: 28.65       
    0:      index: 70          
    1:        x^0: 1           
    2:        x^1: 3.5         
    3:        x^2: 12.25       
    4:        x^3: 42.88       
Warning in tkplotevent.onclick(): Can not find axes