# def get_k_fold_train_indexes(test_index_tuples, length, k=10) -> list: # for tuple in test_index_tuples: # def k_fold_estimate(x_array_train, k=10): defrandom_reservation(): classifiers:list=[clf_GNB,clf_KNN,clf_RF,clf_GB,clf_MLP] sort_list=[] times=10 count_prob=[0.0...
sort_list = [] forclfinclassifiers: result = get_average_accuracy(times=10,estimate_scale=98,clf=clf) print("in average result with:", clf, result) sort_list.append((result, clf)) # print(sort_list) sort_list.sort(key=lambdatuple:tuple[0], reverse=True) foriteminsort_list: print(i...
#建模环节,用list把所有模型装起来,设置好各模型的参数 names = ["Nearest Neighbors", "Logistic","Decision Tree", "Random Forest", "AdaBoost", "GBDT","SVM","MLPClassifier"] classifiers = [ KNeighborsClassifier(3), LogisticRegressionCV(), DecisionTreeClassifier(max_depth=5), RandomForestClassifi...
sort_list=[] forclfinclassifiers: result=get_average_accuracy(times=10,estimate_scale
weights : array-like, shape = [n_classifiers], optional (default=None Sequence of weights (float or int) to weight the occurrences of predicted class labels (hard voting) or class probabilities before averaging (soft voting). Uses uniform weights if None. ...
sclf = StackingCVClassifier(classifiers=[pipe1, pipe2], # 两个基分类器区别在于使用特征不同 meta_classifier=LogisticRegression(), random_state=42) sclf.fit(X, y) 下面我们可以画ROC曲线: from sklearn import model_selection from sklearn.linear_model import LogisticRegression from sklearn.neighbors...
1、estimators_ : list of classifiers,拟合的子估计器的集合。 2、classes_ : array of shape = [n_classes],类的标签。 3、n_classes_ : int,类的数量。 4、estimator_weights_ : array of floats,在提升的总体效果中,每个估计器的权重。
.svmimportOneClassSVMimportmatplotlib.pyplotaspltimportmatplotlib.font_managerfromsklearn.datasetsimportload_boston# 获取数据X1 = load_boston()['data'][:, [8,10]]# two clustersX2 = load_boston()['data'][:, [5,12]]# "banana"-shaped# 定义分类器classifiers = {"Empirical Covariance": ...
#print numpy.nonzero(train_labels[4, :])[0].tolist() """ Rows: Docs ; Columns: Terms print vectorised_test_documents_tfidf[[1, 3], :].shape print vectorised_test_documents_tfidf.shape print len(train_docs), " : ", vectorised_train_documents_tfidf.shape print len(test_docs)...
classdoc_ids = numpy.nonzero(train_labels_complement[:, i])[0].tolist() compl_term_freq[i] = get_TF(vectorizer_tf, vectorised_train_documents_tf, classdoc_ids) #Convert to Probability & Perform Jelinek-Mercer Smoothing term_prob = {} ...