9))val_curve=ValidationCurve(KNeighborsClassifier(),param_name='n_neighbors',param_range=n_neighbo...
Python的话为了简便起见用了手写数字数据集来先降维后预测,同样也是分为7:3分割数据集的模式和交叉验证的模式,选取k=1-20,分别计算准确率,取使其最高的k。 fromsklearnimportdatasetsfromsklearn.decompositionimportPCAfromsklearn.neighborsimportKNeighborsClassifierfromsklearn.model_selectionimporttrain_test_split,cr...
classifier = KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=10, p=2, weights='uniform') 超参数需要自己尝试。 其他 fromsklearn.neighborsimportKNeighborsClassifierfromsklearn.linear_modelimportLogisticRegressionfromsklearn.ensembleimp...
for w in weights: knn = KNeighborsClassifier(n_neighbors=k, weights=w,) sm=cross_val_score(knn,X,Y,scoring='accuracy',cv=6).mean()#交叉验证cross_val_score选择最合适的参数,分类scoring='accuracy'评价指标 result[w+str(k)]=sm print(result) print(list(result)[np.array(list(result.values...
print('总方差占比:{}'.format(sum(pca.explained_variance_ratio_))) return pcaTrainData, pcaTestData 5、模型选择 - KNN 调用sklearn.neighbors中的KNeighborsClassifier()分类器,k为默认值5 # 模型选择 - KNN def trainModel(trainData, trainLabel): ...
classifierResult = classify(testData[i], trainData, trainLabel,1) resultList.append(classifierResult)print("the classifier for %d came back with: %d, the real answer is: %d"% (i, classifierResult, testLabel[i]))if(classifierResult != testLabel[i]): errorCount +=1.0print("\nthe total...
KNN_PCA_TRAIN_SIZE=200000KNN_PCA_TEST_SIZE=200fromsklearn.neighborsimportKNeighborsClassifiertemp=[]foriin[1,5]:knn_pca=KNeighborsClassifier(n_neighbors=i,n_jobs=8)knn_pca.fit(X_train_pca[:KNN_PCA_TRAIN_SIZE],y_train[:KNN_PCA_TRAIN_SIZE])train_score_pca=knn_pca.score(X_train_pca[...
Classifier SVM DT KNN MLP Rank Sampler AUC Sampler AUC Sampler AUC Sampler AUC 1 KNNOR .8892 LVQ-SMOTE [31] .8686 polynom-fit-SMOTE [25] .905 KNNOR .9055 2 G-SMOTE [29] .8815 KNNOR .8594 ProWSyn [26] .9043 polynom-fit-SMOTE [25] .8899 3 polynom-fit-SMOTE [25] .8729 ...
knn =KNeighborsClassifier(n_neighbors=n) knn.fit(X_train_pca[:,:component], y_train_pca) score = knn.score(X_test_pca[:,:component], y_test_pca) #predict = knn.predict(X_test_pca[:,:component]) scores[component][n] = scoreprint('Components = ', component,', neighbors = ', ...
, but they are not suitable for this task due to data structure adopted, not graph based. After the creation of the graph, the application of some heuristic allows to extrapolate useful information through the graph for improving the performance of the retrieval system or the image classifier....