Naive Model

Naive model

A simple OLS model with temperature and air humidity, without any transformation or feature engineering -no thinking at all-.

⏩ stepit 'naive': Starting execution of `strom.modelling.assess_model()` 2026-04-12 13:37:03

⏩ stepit 'get_single_split_metrics': Starting execution of `strom.modelling.get_single_split_metrics()` 2026-04-12 13:37:04

✅ stepit 'get_single_split_metrics': Successfully completed and cached [exec time 0.0 seconds, cache time 0.0 seconds, size 1.0 KB] `strom.modelling.get_single_split_metrics()` 2026-04-12 13:37:04

⏩ stepit 'cross_validate_pipe': Starting execution of `strom.modelling.cross_validate_pipe()` 2026-04-12 13:37:04

[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.

[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed:    2.1s finished

✅ stepit 'cross_validate_pipe': Successfully completed and cached [exec time 2.1 seconds, cache time 0.0 seconds, size 2.2 KB] `strom.modelling.cross_validate_pipe()` 2026-04-12 13:37:06

✅ stepit 'naive': Successfully completed and cached [exec time 2.2 seconds, cache time 0.0 seconds, size 15.7 KB] `strom.modelling.assess_model()` 2026-04-12 13:37:06

Metrics

	Single Split		CV
	train	test	test	train
MAE - Mean Absolute Error	3.340488	3.162185	2.826249	3.521145
MSE - Mean Squared Error	25.947551	24.493693	12.676356	28.646140
RMSE - Root Mean Squared Error	5.093874	4.949110	3.426344	5.349381
R2 - Coefficient of Determination	0.721589	0.749748	-8.271677	0.710541
MAPE - Mean Absolute Percentage Error	0.385556	0.325290	0.541985	0.374222
EVS - Explained Variance Score	0.721589	0.749876	-2.079990	0.710541
MeAE - Median Absolute Error	2.667506	2.272731	2.553267	2.588375
D2 - D2 Absolute Error Score	0.517747	0.587819	-1.510111	0.504172
Pinball - Mean Pinball Loss	1.670244	1.581092	1.413125	1.760573

Scatter plot matrix

Observed vs. Predicted and Residuals vs. Predicted

Check for …

check the residuals to assess the goodness of fit.

white noise or is there a pattern?
heteroscedasticity?
non-linearity?

Normality of Residuals:

Check for …

Are residuals normally distributed?

Leverage

Scale-Location plot

Residuals Autocorrelation Plot

Residuals vs Time

TODOs

Clearly the naive model is not a good fit (as expected).

Listing 1

It needs a polynomial with the temperature (second order perhaps)
Check if the association with relative humidity, if it is just an artifact of the correlation with temperature, or if there might something meaningful going on there
and bring other climatic data, rainfall and snowfall might be relevant

Naive Model, but using statsmodels …

                            OLS Regression Results                            
==============================================================================
Dep. Variable:                     wd   R-squared:                       0.737
Model:                            OLS   Adj. R-squared:                  0.737
Method:                 Least Squares   F-statistic:                     1963.
Date:                Sun, 12 Apr 2026   Prob (F-statistic):               0.00
Time:                        13:37:10   Log-Likelihood:                -4259.4
No. Observations:                1405   AIC:                             8525.
Df Residuals:                    1402   BIC:                             8541.
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
const         27.9188      1.219     22.899      0.000      25.527      30.310
tt_tu_mean    -1.1363      0.019    -59.272      0.000      -1.174      -1.099
rf_tu_mean    -0.0612      0.014     -4.300      0.000      -0.089      -0.033
==============================================================================
Omnibus:                      979.383   Durbin-Watson:                   0.631
Prob(Omnibus):                  0.000   Jarque-Bera (JB):            21072.270
Skew:                           2.959   Prob(JB):                         0.00
Kurtosis:                      21.026   Cond. No.                         737.
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
R-squared: 0.7368832968577501

Naive model, but using scikit-learn without pipeline …