numpy exploratory-data-analysis machine-learning-algorithms prediction pandas data-visualization seaborn lightgbm matplotlib data-preprocessing decision-tree-classifier scikitlearn-machine-learning random-forest-classifier xgboost-model knn-classifier svc-model catboost-model logistic-regression-classifier mlxtend pcos...
random_state=SEED) print("拆分前:",X.shape,y.shape) aKFold= KFold(n_splits=5, shuffle=True, random_state=SEED)#返回的是一个KFold实例,shuffle=True则不是固定的下标fortrain_index, test_indexinaKFold.split(X):#train_index, test_index返回的是下标print("%s %s"%(train_index, test_...
from sklearn.svm import SVC # 创建一个模拟数据集 X, y = make_classification(n_samples=100, n_features=20, n_informative=2, n_redundant=10, random_state=42) # 初始化SVM分类器 svc = SVC(kernel="linear", C=1) # 初始化RFECV实例 rfe = RFECV(estimator=svc, step=1, cv=5) # 在数...
random_state) model = CalibratedClassifierCV(base_estimator=mod, method='isotonic', cv=kf) lb = LabelEncoder() lb.fit(self.labels) y = lb.transform(y) else: model = LinearSVR(epsilon=self.params["epsilon"], C=self.params["C"], loss=self.params["loss"], dual=self.params["dual"],...
importsklearnimportshapfromsklearn.model_selectionimporttrain_test_split# print the JS visualization code to the notebookshap.initjs()# train a SVM classifierX_train,X_test,Y_train,Y_test=train_test_split(*shap.datasets.iris(),test_size=0.2,random_state=0)svm=sklearn.svm.SVC(kernel='rbf...
(f_regression, k=5)12clf = svm.SVC(kernel='linear')13pipe = Pipeline([('anova', anova_filter), ('svc', clf)])1415#设置anova的参数k=10,svc的参数C=0.1(用双下划线"__"连接!)16pipe.set_params(anova__k=10, svc__C=.1)17pipe.fit(X, y)1819prediction =pipe.predict(X)2021pipe....
The so-called kernel similarity function is used to design the non-linear SVM [38]. Currently, there are textbook style implementations of two popular linear SVM algorithms: Pegasos [39], Dual Coordinate Descent. LIBSVM developed by the Information Engineering Institute of Taiwan University is the...
SVM SVC, Kernel linear, degree=8, gamma = “auto” NB Random_state=0, Metric_param=dict DT Random_state=0, Max_depth=15 3.6. Proposed method The proposed model is constructed using neurons as its fundamental element. When analyzing sequential data, this method is particularly effective becaus...
0.9 kernel: type: choice values: - rbf - linear - poly coef0: type: uniform min_value: 0.1 max_value: 1 sampling_algorithm: random objective: goal: minimize primary_metric: training_f1_score limits: max_total_trials: 20 max_concurrent_trials: 10 timeout: 7200 # Score test data predict...
def get_full_rbf_svm_clf(train_x, train_y, c_range=None, gamma_range=None): param_grid = dict(gamma=gamma_range, C=c_range) cv = StratifiedShuffleSplit(n_splits=2, test_size=0.2, random_state=42) grid = GridSearchCV(SVC(cache_size=1024), param_grid=param_grid, cv=cv, n_jo...