Python Programming
Task description: C and Gamma are the parameters for a nonlinear support vector machine (SVM). The goal of SVM is to find a hyperplane that would leave the widest possible "cushion" between input points from two classes. There is a tradeoff between "narrow cushion, little / no mistakes" and "wide cushion, quite a few mistakes". Small C makes the cost of misclassificaiton low ("soft margin"), thus allowing more of them for the sake of wider "cushion". Large C makes the cost of misclassification high ('hard margin"), thus forcing the algorithm to explain the input data stricter and potentially overfit. The goal is to find the balance between "not too strict" and "not too loose". Gamma is the parameter of a Gaussian Kernel (to handle non-linear classification). Cross-validation and resampling, along with grid search, are good ways to finding the best C and gamma. The following figure shows the process of finding the best parameters using grid search and cross- validation. parameter grid data set training data test data cross-validation best parameters retrained model final evaluation In this task, you are given a dataset, a parameter grid and cross-validation (CV) number, and try to find the best parameters C and gamma of SVM. You are given: Dataset: from sklearn.datasets import load_digits digits = load_digits() X = digits.data y = digits.target Parameter grid: param_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100], gamma': [0.001, 0.01, 0.1, 1, 10, 100]} . Other parameters of your settingYou are asked to: use Grid search with cross-validation to fit the data: SVC is the model, cv=5, return_train_score=True . set random_state=0 when split train and test sets . print grid_search.score of test dataset . print grid_search.best_params_ . print grid_search.best_score_ print grid_search.best_estimator_ Please refer to the textbook 5.2.3 Grid Search with Cross-Validation of chapter 5 "Model evaluation and improvement" and the textbook github site https://github.com/amueller/introduction to ml with_python/blob/master/05-model-evaluation- and-improvement.ipynb (Grid search part). Sample output as shown in the following figures are for demonstration purposes only. Yours might be different from the provided. Parameter grid: {'C': [0.001, 0.01, 0.1, 1, 10, 100], "gamma": [0.001, 0.01, 0.1, 1, 10, 100]} Test set score: 0.99 Best parameters: {'C' : 10, 'gamma" : 0.091} Best cross-validation score: 0.99 Best estimator: SVC(C=10, cache_size=200, class_weight=None, coef0=0.0, decision_function_shape='ovr', degree=3, gamma=0.001, kernel='rof", max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False)
