y_digits=digits.target svc= svm.SVC(C=1, kernel='linear') svc.fit(X_digits[:-100], y_digits[:-100]).score(X_digits[-100:], y_digits[-100:]) 为了获得一个更好的预测精确度度量,我们可以把我们使用的数据折叠交错地分成训练集和测试集: importnumpy as np X_folds= np.array_split(X_d...
clf=svm.SVC(kernel='linear',C=1) scores=cross_val_score(clf,X,y,cv=5) scores array([0.96666667,1. ,0.96666667,0.96666667,1. ]) #得分估计的平均分数和95%置信区间print("Accuracy:%0.2f+(+/-%0.2f)"%(scores.mean(),scores.std()*2)) ...
svc = svm.SVC(C=1, kernel='linear') svc.fit(X_digits[:-100], y_digits[:-100]).score(X_digits[-100:], y_digits[-100:]) 1. 2. 3. 4. 5. 6. 为了获得一个更好的预测精确度度量,我们可以把我们使用的数据折叠交错地分成训练集和测试集: import numpy as np X_folds = np.array_sp...
5.支持向量机SVM from sklearn.svm import SVC model = SVC(C=1.0, kernel=’rbf’, gamma=’auto’) """参数 --- C:误差项的惩罚参数C gamma: 核相关系数。浮点数,If gamma is ‘auto’ then 1/n_features will be used instead. """ 1. 2. 3. 4. 5. 6. 7. 6.k近邻算法KNN from skl...
Compare SVM mode yoga movement classification accuracy with Linear kernel, Polynomial kernel, RBF (Radial Basis Function) kernel, LSTM with accuracy up to 98%. In addition, it also supports adjusting the practitioner's movements according to standard movements. machine-learning computer-vision deep-le...
svc = SVC(kernel="linear", C=1) # 初始化RFECV实例 rfe = RFECV(estimator=svc, step=1, cv=5) # 在数据集上拟合RFECV实例 rfe.fit(X, y) # 打印特征排名,数字越大表示特征越重要 print("Feature ranking: ", rfe.ranking_) ``` 这个代码首先创建了一个模拟数据集,然后初始化了一个线性SVM分类...
importtimefromsklearn.svmimportSVCfromsklearn.model_selectionimporttrain_test_split,GridSearchCVstart=time.time()cross_valid_scores={}parameters={"C":0.1,"kernel":"linear","gamma":"scale",}model_svc=SVC(random_state=2200,class_weight="balanced",probability=True,)model_svc.fit(x_train,y_trai...
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...
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...
The SVC model similarly was divided into four variants with differing kernel functions: RBF (SVC1), linear (SVC2), polynomial (SVC3) and sigmoid (SVC4). In each variant, the penalty parameter of the error term was set to C=1.0. Consequently, a total of 11 models were used: the prope...