hi ,
i need a computer enginnering help me in my dissertation which i compzred betwwen machine learning models such as logistic models l, random freedom,MDA , support vector system , data envolpment ,
deep learning :
auto - encoder , recursive deep learning , extreme learning machine , convertional neural network , to predict financial distress ,
and explain to me this models, and how the classiffier data and filter it , this study on egyptian listed companies
To start, let's go through each of the models you mentioned and their characteristics:
In terms of classifying and filtering data, each model has its own approach. Logistic regression, for example, uses a threshold probability to determine whether a data point belongs to a certain class. Random Forest combines the predictions of multiple decision trees to improve accuracy. SVMs identify a hyperplane that maximizes the margin between different classes of data points. DEA calculates the efficiency of each decision-making unit based on its input and output variables. Auto-encoders learn to reconstruct input data from a compressed representation. Recursive deep learning models process sequences of inputs, and ELMs and CNNs use neural networks to identify patterns in data.
In your dissertation, you can compare the performance of these models in predicting financial distress in Egyptian listed companies by evaluating metrics such as accuracy, precision, recall, and F1 score. You can also analyze the features that are most important for each model in making predictions, and discuss the potential limitations and challenges of using these models in practice.
yes , this will be my dissertation , to compare models , as i am financial student i need someone to run me data to compare for each method , could you help me , as i correct data and run it ?
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