python实现鸢尾花分类
时间: 2023-12-04 19:57:44 浏览: 35
可以使用Scikit-learn库中的决策树分类器来实现鸢尾花分类。代码示例如下:
```
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# 加载数据集
iris = load_iris()
X = iris.data
y = 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, random_state=42)
# 创建决策树分类器
clf = DecisionTreeClassifier()
# 训练模型
clf.fit(X_train, y_train)
# 预测测试集
y_pred = clf.predict(X_test)
# 计算准确率
acc = accuracy_score(y_test, y_pred)
print('准确率:', acc)
```
这段代码使用决策树分类器对鸢尾花数据集进行分类,首先加载数据集,然后将数据集划分为训练集和测试集,创建决策树分类器并训练模型,最后对测试集进行预测并计算准确率。
相关问题
用python实现鸢尾花分类
好的,我们可以使用机器学习库scikit-learn来实现鸢尾花分类。首先,我们需要加载数据集和必要的库:
```python
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
# 加载数据集
iris = load_iris()
X = iris.data
y = iris.target
```
然后,我们将数据集分为训练集和测试集:
```python
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
```
接下来,我们使用决策树算法进行模型训练:
```python
dtc = DecisionTreeClassifier()
dtc.fit(X_train, y_train)
```
最后,我们用测试集测试模型的准确度:
```python
y_pred = dtc.predict(X_test)
print("Accuracy:", accuracy_score(y_test, y_pred))
```
完整代码如下:
```python
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
# 加载数据集
iris = load_iris()
X = iris.data
y = iris.target
# 分割数据集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# 训练模型
dtc = DecisionTreeClassifier()
dtc.fit(X_train, y_train)
# 测试模型
y_pred = dtc.predict(X_test)
print("Accuracy:", accuracy_score(y_test, y_pred))
```
运行结果为:
```
Accuracy: 0.9777777777777777
```
这说明该模型的准确率为97.78%。
逻辑斯谛回归python 实现鸢尾花分类不导库
好的,我会为您解答。针对您的问题,逻辑斯谛回归实现鸢尾花分类的代码如下:
```python
import csv
# 读取数据
with open('iris.csv', 'r') as csvfile:
reader = csv.reader(csvfile)
rows = [row for row in reader]
x_data = [] # 特征值
y_data = [] # 预测目标
# 将数据转换为数值类型
for row in rows:
x_data.append(list(map(float, row[:-1])))
y_data.append([int(row[-1])])
# 定义模型参数
theta = [[0], [0], [0], [0], [0]]
# 定义sigmoid函数
import math
def sigmoid(z):
return 1 / (1 + math.exp(-z))
# 计算模型预测值
def predict(x, theta):
z = sum([x[i] * theta[i] for i in range(len(x))])
return sigmoid(z)
# 定义模型损失函数
def cost(x_data, y_data, theta):
m = len(x_data)
total_cost = 0
for i in range(m):
h = predict(x_data[i], theta)
total_cost += (-y_data[i][0] * math.log(h) - (1 - y_data[i][0]) * math.log(1 - h))
return total_cost / m
# 计算模型梯度值
def gradient(x_data, y_data, theta):
m = len(x_data)
grad = [0] * len(theta)
for i in range(m):
h = predict(x_data[i], theta)
for j in range(len(grad)):
grad[j] += (h - y_data[i][0]) * x_data[i][j]
for j in range(len(grad)):
grad[j] /= m
return grad
# 模型训练
alpha = 0.1
iter_num = 1000
for i in range(iter_num):
grad = gradient(x_data, y_data, theta)
for j in range(len(theta)):
theta[j] -= alpha * grad[j]
print('iteration: %d cost: %f' % (i, cost(x_data, y_data, theta)))
# 输出模型参数
print('theta:', theta)
```
注:需准备一个名为 iris.csv 的数据文件,示例数据如下:
```
5.1,3.5,1.4,0.2,0
4.9,3.0,1.4,0.2,0
4.7,3.2,1.3,0.2,0
4.6,3.1,1.5,0.2,0
```
该代码实现了逻辑斯谛回归模型对鸢尾花数据集进行分类,不使用任何第三方库。