Hello Wayne,
To answer your question on how you can identify data characteristics which influence the success of a model.
You can perform some basic Exploratory Data Analysis (EDA) to understand the distributions of your parameters and outcomes, identify outliers, and see if there are any obvious patterns or correlations.
- Use "histogram", "boxplot", or "scatter" functions to visualize the distributions of your parameters and outcomes.
- Use "corrplot" to visualize correlations between parameters and between parameters and outcomes.
With many input parameters, it's crucial to identify which ones significantly impact the model's outcome. Feature selection techniques can help reduce dimensionality and focus on the most influential variables.
- Use "sequentialfs" (sequential feature selection) to identify the most important features. This function can help you find a subset of the input variables that most effectively predict the outcome.
- Consider using principal component analysis (PCA) with "pca" to reduce dimensionality and possibly uncover underlying patterns in your data.
Here are the links for some of the mentioned functions:
- scatter: https://www.mathworks.com/help/matlab/ref/scatter.html
- corrplot: https://www.mathworks.com/help/econ/corrplot.html
- sequentialfs: https://www.mathworks.com/help/stats/sequentialfs.html
- pca: https://in.mathworks.com/help/stats/pca.html
Here are some examples that might be useful in your case:
- For feature selection: https://www.mathworks.com/help/stats/selecting-features-for-classifying-high-dimensional-data.html
- For classification : https://www.mathworks.com/help/stats/classification-example.html
- For cross validation: https://www.mathworks.com/help/stats/crossval.html
