Changed categorical slot comparison to be case insensitive. Exit training when transformer_size is not divisible by the number_of_attention_heads parameter and update the transformer documentations. Improved Documentation# Update compatibility matrix between Rasa-plus and Rasa Pro services. [3.4.0] - ...
we propose to exclude from the study students for which the data is not complete. Thus, students presenting one or more missing values were not considered for the training of the predictive models. Moreover, categorical features were one-hot encoded, while ordinal features...
For the numerical variables, the median is the dividing line. The choice of the median is justified as it splits the data into two halves, thus ensuring the comparability of the groups (Hauke & Kossowski, 2011). Logical divisions were made for the categorical variables. For instance, if a...
We applied a stratified random sample equating to 0.1% of the data into the random-forest model (n = 2500). This sample size was more than the minimum number of samples needed (1000 = 0.04%) to ensure results would be within the 95% confidence interval with a sampling error of...
Bivariate analyses were conducted using a two-tailed Student’s t-test for continuous data and a two-tailed χ2 or Fisher’s exact test for categorical data when appropriate. All these statistical analyses were performed using IBM SPSS Statistics 20.0 (IBM Corp., Armonk, NY, USA), and p-...
Serial (RCM411): The serial number or identifier of the data collection device (data logger). State: is a categorical variable that indicates the overall condition of the road surface—Dry, Moist, Wet, Icy, Snowy, and Slushy—with values from 1–6. To examine more information from the dat...
Serial (RCM411): The serial number or identifier of the data collection device (data logger). State: is a categorical variable that indicates the overall condition of the road surface—Dry, Moist, Wet, Icy, Snowy, and Slushy—with values from 1–6. To examine more information from the dat...
(AUC value). The numerical range of AUC was set at 0~1, and the larger the numerical value, the higher the accuracy of the model. Theoretically, when the AUC value was 0.5~0.6, the model had no prediction ability; at 0.6~0.7, the prediction ability was poor; at 0.7~0.8, the ...