我正在使用WEKA工具进行多项Logistic回归。出于实验目的，我正在研究天气示例并尝试使用逻辑回归来预测Outlook特征。 Plz有助于解释结果。特别是系数的使用以及如何构造预测方程。 Outlook功能可以包含三个值（Sunny，Overcast，Rainy）。
=== Run information ===
Scheme: weka.classifiers.functions.Logistic -R 1.0E-8 -M -1
Relation: weather.symbolic
Instances: 14
Attributes: 5
outlook
temperature
humidity
windy
play
Test mode: 10-fold cross-validation
=== Classifier model (full training set) ===
Logistic Regression with ridge parameter of 1.0E-8
Coefficients...
Class
Variable sunny overcast
================================================================
temperature=hot 14.3585 45.2229
temperature=mild -6.0885 -34.2035
temperature=cool -7.0523 -4.1787
humidity 1.1354 -31.1848
windy 0.6535 -29.4463
play 0.4818 -90.7874
Intercept 4.6263 50.8973
Odds Ratios...
Class
Variable sunny overcast
================================================================
temperature=hot 1721195.3778 4.3658888975323775E19
temperature=mild 0.0023 0
temperature=cool 0.0009 0.0153
humidity 3.1125 0
windy 1.9223 0
play 1.6191 0