使用python编程，调用sklearn中的模块方法，分别用logistics回归、SVM、BP神经网络对sklearn数据库中鸢尾花数据（去掉某一个类，剩下的两个类）进行二分类，并简要分析分类性能。

对于二分类任务，我们可以使用Logistic回归、支持向量机（SVM）和BP神经网络等方法进行分类。下面是使用这些方法对鸢尾花数据进行二分类的示例代码：

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

# 加载鸢尾花数据集
iris = datasets.load_iris()
X = iris.data
y = iris.target

# 选择两个类别进行二分类（去掉某一个类）
class_to_remove = 0  # 选择去掉第一个类别
X = X[y != class_to_remove]
y = y[y != class_to_remove]

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Logistic回归分类器
lr = LogisticRegression()
lr.fit(X_train, y_train)
lr_pred = lr.predict(X_test)

# 支持向量机分类器
svm = SVC()
svm.fit(X_train, y_train)
svm_pred = svm.predict(X_test)

# BP神经网络分类器
nn = MLPClassifier(hidden_layer_sizes=(10,), max_iter=1000)
nn.fit(X_train, y_train)
nn_pred = nn.predict(X_test)

# 分类性能评估
lr_accuracy = accuracy_score(y_test, lr_pred)
svm_accuracy = accuracy_score(y_test, svm_pred)
nn_accuracy = accuracy_score(y_test, nn_pred)

lr_precision = precision_score(y_test, lr_pred)
svm_precision = precision_score(y_test, svm_pred)
nn_precision = precision_score(y_test, nn_pred)

lr_recall = recall_score(y_test, lr_pred)
svm_recall = recall_score(y_test, svm_pred)
nn_recall = recall_score(y_test, nn_pred)

lr_f1 = f1_score(y_test, lr_pred)
svm_f1 = f1_score(y_test, svm_pred)
nn_f1 = f1_score(y_test, nn_pred)

print("Logistic回归分类器性能:")
print("准确率: {:.2f}".format(lr_accuracy))
print("精确度: {:.2f}".format(lr_precision))
print("召回率: {:.2f}".format(lr_recall))
print("F1-score: {:.2f}".format(lr_f1))
print()

print("支持向量机分类器性能:")
print("准确率: {:.2f}".format(svm_accuracy))
print("精确度: {:.2f}".format(svm_precision))
print("召回率: {:.2f}".format(svm_recall))
print("F1-score: {:.2f}".format(svm_f1))
print()

print("BP神经网络分类器性能:")
print("准确率: {:.2f}".format(nn_accuracy))
print("精确度: {:.2f}".format(nn_precision))
print("召回率: {:.2f}".format(nn_recall))
print("F1-score: {:.2f}".format(nn_f1))

上述代码中，我们首先加载鸢尾花数据集，并选择两个类别进行二分类。然后，我们使用逻辑回归、支持向量机和BP神经网络进行训练和预测，并计算了准确率、精确度、召回率和F1-score等性能指标。

希望以上代码对您有帮助！如果有任何疑问，请随时提问。