## 3 Results
### Association Metrics
#### 3.1.2 M2 Based Feature Association  
The following section describes the experimental setup of Market Basket Analysis (MBA) techniques in frequent item set mining [22]. The relationships between features are examined, with a particular focus on identifying frequent patterns according to _M2_, defined in Section 2.2.1. The most important metric of this analysis is support, which indicates how frequent a combination is. Therefore, when analyzing Table 5 which corresponds to the association metrics obtained by selecting the entire set of all the combinations described in the previous section, it is observable that the best-positioned combination of cum precipitation and length has a very small support of 0.1. Despite the individual presence of _cum precipitation_ being 0.47, the goal of this study is to extract the most robust or significant feature combinations as a whole, not individually. Therefore, the supports of the antecedents as well as the consequents do not acquire much importance in this analysis. Moreover, the interpretation of the lift and leverage metrics indicates a high independence between the variables in question. On the contrary, the correlations detailed in Figure A1, demonstrate the high dependence between accumulated features and temperature-derived features. _lat_ and _sea level_ seem to be totally independent between them due to their null correlation, but _lat_ is slightly correlated to _precip cum_, with a correlation of 0.33, and it appears related as an antecedent or consequent with numerous features. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature** & **Weight** & **Feature** & **Weight** & **Feature** & **Weight** \\ \hline precip cum & 27,053 & mean temp & 8285 & NIR & 4885 \\ lat & 24,946 & sur ref b03 & 7929 & NIRcum & 3960 \\ max temp & 12,051 & dewp temp & 7905 & EVI & 3803 \\ sea level & 11,301 & EVIcum & 7729 & NDVI & 3293 \\ slope & 11,141 & REDcum & 6958 & wind u & 3271 \\ min temp & 10,570 & SolarZenith & 6790 & wind v & 2700 \\ surface & 8699 & lon & 5897 & total precip & 2179 \\ pressure & 8602 & sur ref b07 & 5611 & view zenith & 1618 \\ NDVIcum & 8322 & Rel azimuth & 5295 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Efficiency metric-based weight accumulation for features. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Antecedents** & **Consequents** & **a Sup** & **c Sup** & **Sup** & **Conf** & **Lift** & **Lev** \\ \hline precip cum & lon & 0.47 & 0.20 & 0.10 & 0.20 & 1.02 & 0.00 \\ lon & precip cum & 0.20 & 0.47 & 0.10 & 0.49 & 1.02 & 0.00 \\ lat & slope & 0.25 & 0.35 & 0.09 & 0.37 & 1.03 & 0.00 \\ slope & lat & 0.35 & 0.25 & 0.09 & 0.25 & 1.03 & 0.00 \\ slope & GDD & 0.35 & 0.24 & 0.09 & 0.25 & 1.08 & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: RMSE threshold 0.6549, 100%. However, the total number of analyzed combinations is very large, with around 150,000 possible combinations (see the previous section). Consequently, the number of combinations must be analyzed by selecting the best combinations of 75%, 50%, 25%, 10%, and 1% of the entire data set. The association metrics corresponding to these selections and the RMSE limit are described in Tables 6-10, respectively. Indeed, Tables 6-8 depict results corresponding to the 75%, 50%, and 25% percentages of the data set, where the support maximum values depict values below 0.17 despite the data set reduction. In the best case, visible in Table 8, the support has risen to 0.16 with the combination of _slope_ and _latitude (lat)_ being the most frequent combination. However, Tables 9 and 10 (corresponding to 10% and 1% of the data set) visualize an increase in the support metric, with respective maximum values of 0.23 and 0.33, and both cases highlight the same most representative feature combination: _maximum temperature_ and _accumulated precipitation_. The following most representative combination is that of latitude and slope in Table 9, while in Table 10, it is the couple of _latitude_ and _sea level_. In both cases, these are geographic characteristics.