scikit-learn-examples：使用Scikit-learn探索Logistic回归，MLP和SVM

# Scikit-learn examples Exploration on Logistic Regression, MLP, and SVM using Scikit-learn ### Files - main.py : main program - config.py : config, argument - util.py : related to data preprocessing - model.py : model - img/ : directory to save loss function graphs (for linear regression) ### Dataset - Breast Cancer - Diabetes - Digit - Iris - Wine ### Run the code 1. linear regression ``` python main.py -model=lr # to show the plot on the fly python main.py -model=lr -show=True ``` 2. NN ``` python main.py -model=nn ``` 3. SVM with a linear kernel ``` python main.py -model=svm_linear ``` 4. SVM with a RBF kernel ``` python main.py -model=svm_rbf ``` ### Results The graphs show changes in train and test losses over epochs <p float="left"> <img src="img/breast-cancer_lr.png" width=200/> <img src="img/diabetes_lr.png" width=200/> <img src="img/digit_lr.png" width=200/> <img src="img/iris_lr.png" width=200/> <img src="img/wine_lr.png" width=200/> </p> ### Note COMP5212 - Machine Learning Programming Assignment 1 in HKUST