感知机鸢尾花分类python

好的，针对您的问题，以下是感知机鸢尾花分类的Python代码：

from sklearn.datasets import load_iris
from sklearn.linear_model import Perceptron
from sklearn.model_selection import train_test_split

# 加载数据集
iris = load_iris()
X, y = iris.data, iris.target

# 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)

# 创建感知机分类器对象，并进行训练
clf = Perceptron()
clf.fit(X_train, y_train)

# 在测试集上进行预测
score = clf.score(X_test, y_test)
print('分类器在测试集上的准确率为：', score)

相关问题

多层感知机鸢尾花分类python代码

好的，以下是多层感知机鸢尾花分类的 Python 代码：

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPClassifier

# 加载数据集
iris = load_iris()
X = iris.data
y = iris.target

# 数据集划分
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)

# 数据标准化
sc = StandardScaler()
sc.fit(X_train)
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)

# 多层感知机模型训练
mlp = MLPClassifier(hidden_layer_sizes=(10,), max_iter=1000)
mlp.fit(X_train_std, y_train)

# 模型评估
print('训练集准确率：', mlp.score(X_train_std, y_train))
print('测试集准确率：', mlp.score(X_test_std, y_test))

鸢尾花分类python感知机

好的，鸢尾花分类问题可以使用感知机模型进行解决。感知机是一种二分类模型，可以用于解决线性可分问题。对于鸢尾花分类问题，我们可以将其转化为二分类问题，即将三个类别中的两个类别作为一类，另一个类别作为另一类。然后使用感知机模型进行训练和预测。

以下是使用Python实现感知机模型的示例代码：

import numpy as np

class Perceptron:
    def __init__(self, learning_rate=0.1, max_iter=1000):
        self.learning_rate = learning_rate
        self.max_iter = max_iter

    def fit(self, X, y):
        n_samples, n_features = X.shape
        self.weights = np.zeros(n_features)
        self.bias = 0
        for _ in range(self.max_iter):
            for i in range(n_samples):
                if y[i] * (np.dot(X[i], self.weights) + self.bias) <= 0:
                    self.weights += self.learning_rate * y[i] * X[i]
                    self.bias += self.learning_rate * y[i]

    def predict(self, X):
        return np.sign(np.dot(X, self.weights) + self.bias)

# 加载数据集
from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data[:100, :2]
y = iris.target[:100]
y[y == 0] = -1

# 训练模型
model = Perceptron()
model.fit(X, y)

# 预测新数据
new_X = np.array([[5.1, 3.5], [4.9, 3.0]])
print(model.predict(new_X))