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In the two previous projects we saw how we could use a decision tree and then a random forest to improve the prediction of diabetes. We have reached a point where we need to improve. Can boosting be the best alternative to optimize the results?
Boosting is a sequential composition of models (usually decision trees) in which the new model aims to correct the errors of the previous one. This view may be useful in this data set, since several of the assumptions studied in the module are met.
In this project you will focus on this idea by training the dataset to improve the .
Loads the processed dataset from the previous project (split into training and test samples and analyzed with EDA).
One way to optimize and improve the results is to generate a boosting so that there is the necessary variety to enrich the prediction. Train it and analyze its results. Try modifying the hyperparameters that define the model with different values and analyze their impact on the final accuracy and plot the conclusions.
Store the model in the corresponding folder.
Make a study now of the three models used, analyze their predictions, the class with the highest prediction accuracy and the one with the lowest. Which of the three models do you choose?