We use Windows to write all the code. The python libraries we used are sklearn, pandas and matplotlib. To train the logistic model on Iris dataset, just run the code “logistic_regression_iris.py” directly and you will see the output (which are the error rate) and the line charts we got. To train the logistic model on Wine dataset, you can run the code “logistic_regression_wine.py” directly and you will see the output (which are the error rate) and the line charts we got. You will also see the results for classifying each instance in the dataset into 3 types of wines. To train the random forest classifier on Iris dataset, just run the code “random_forest_classifier_iris.py” directly and you will see the output (which are the error rate) and the line charts we got. To train the random forest classifier on Breast cancer dataset, just run the code “random_forest_classifier_cancer.py” directly and you will see the output (which are the error rate) and the line charts we got.
This repository is archieved.