RST-ARM-GLM_-Research
Machine learning interpretability is an important concept in the world of data analytics. How can we be able to trust the decisions made on our data by the models we employ on them. The research seeks to use Rough Sets which is an information granulation and feature selection method to sift through attributes of any dataset, then model the selected attributes using apirori association rule mining method to find objectives rule from the selected attributes. The main intention is to make the user see the kind of attribute sthe model is choosing for it's prediction purposes. The reserach starts off with laoding of the datasets and preprocessing them to ensure that the data is clean and free from errors or omissions. Preprocessing was the main hurdle in this research especially the kirogo datasets which had 30min interval weather reading. The structure of the datasets are as follows: Kirogo attributes were: Date/Time;RH(%),Temp(Degrees celcius), Rainfall Kariki attributes: Date, Min Temp,Avg Temp, Max Temp, Min humidiy, Max humidity, Avg humidity, windspeed, precipitation amount and rain factor.
Next I run sample machine learning models on the data set to get insights on the data, this is important as it will form the basis for comparing my proposed model performance against other the standard machine learning models. However the main purpose of the research is to show the link between interpretability via decision rules and weights used in a the Logit model for the Generalized linear model.
The project starts with preprocessing, this is important so as to make the data ready for modeling with our chosen models. Also involved during this process will be exploratory analysis of the data, this is necessary so as that we can see the various statistical distribution aspect of the data before making predictions on it.
This is followed by getting the required features for modelling on the data via the Rough Set feature selection method using greedy heuristic method. Feature selection is key in machine learning interpretability because through it only important features will be selected and this makes interpretability a bit more easier.
Next I run sample machine learning models on the data set to get insights on the data, this is important as it will form the basis for comparing my proposed model performance against other the standard machine learning models. However the main purpose of the research is to show the link between interpretability via decision rules and weights used in a the Logit model for the Generalized linear model.
The project starts with preprocessing, this is important so as to make the data ready for modeling with our chosen models. Also involved during this process will be exploratory analysis of the data, this is necessary so as that we can see the various statistical distribution aspect of the data before making predictions on it.
This is followed by getting the required features for modelling on the data via the Rough Set feature selection method using greedy heuristic method. Feature selection is key in machine learning interpretability because through it only important features will be selected and this makes interpretability a bit more easier.
Secondly from the deduced feature subset gotten by the Rough Set method in step one above, these features will passed through the association rule method to derive decision rules. The selection of rules will be based on the confidence and support metrics used to evaluate how good a rule is on the data it is operating on.
After deducing of the decision rules, a data frame containing the rules will be formulated and then Logit model for classification will be applied on to this rule data-frame. The rule data-frame will contain binary values representing the decision rules choose in step two above.
The decision rule data frame will be used on the logit model to model the decision variables based on the data set used.
The main work in this research will be finding the optimal rules, generating binary values from these rules, coercing them into a dataframe and running these rules on the Logit model. The consideration for using the multinomial model is also highly encouraged and will be done to test the viability of my proposed framework. The framework seeks to show how Rough Set theory can be used as a feature selection method for association rule mining methods.The research also seeks to show how interaction term detection done at the local level with Rough Set model without prior knowledge makes good interpretability metrics for the GLM model.GLM interpretability is through the interpretation of the weights of the features in the data it's working on. However, GLM assumes that interactions among the features are all similar regardless of the values of other features(C.Molnar, 2019), which in reality is not true as there are countless feature interactions in the data.
The decision rule data frame will be used on the logit model to model the decision variables based on the data set used. These deciison rules will be generated using the association rule mining method, here the main work will be tuning the model to get the optimal rules with high support and confidence that can be used for the weather prediction.