要代码使用支持向量机算法判断学员的Python水平(使用课本数据);2)Iris也称鸢尾花卉数据集(使用sklearn自带的数据集),它共有4个属性列和一个品种类别列:sepal length(萼片长度)、sepal width(萼片宽度)、petal length(花瓣长度)、petal width (花瓣宽度),单位都是厘米。3个品种类别(Setosa(山鸢尾),Versicolour(杂色鸢尾),Virginica(维吉尼亚鸢尾)),样本数量150个,每类50个。分别朴素贝叶斯算法、决策树算法、随机森林算法、KNN算法和支持向量机对大量的鸢尾花数据进行分类训练,并比较这几种算法的评估性能,最后识别新的鸢尾花是什么类型。(要求训练集70%,测试集30%)
时间: 2024-02-12 20:08:41 浏览: 71
好的,这是两个问题,我会分别回答。
首先是第一个问题,使用支持向量机算法判断学员的Python水平。以下是使用课本数据的代码示例:
```python
# 导入所需的库
from sklearn import svm
import pandas as pd
# 读取数据
data = pd.read_csv('path/to/data.csv')
# 分离特征和标签
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
# 划分训练集和测试集
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
# 创建SVM分类器并训练模型
clf = svm.SVC(kernel='linear', C=1, random_state=0)
clf.fit(X_train, y_train)
# 在测试集上进行预测并计算准确率
from sklearn.metrics import accuracy_score
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
```
接下来是第二个问题,使用朴素贝叶斯、决策树、随机森林、KNN和支持向量机算法对Iris数据集进行分类训练,并比较这几种算法的评估性能,最后识别新的鸢尾花是什么类型。以下是代码示例:
```python
# 导入所需的库和数据集
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn import svm
from sklearn.metrics import accuracy_score
iris = datasets.load_iris()
# 分离特征和标签
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=0.3, random_state=0)
# 朴素贝叶斯分类器
nb = GaussianNB()
nb.fit(X_train, y_train)
nb_y_pred = nb.predict(X_test)
nb_accuracy = accuracy_score(y_test, nb_y_pred)
print("Naive Bayes Accuracy:", nb_accuracy)
# 决策树分类器
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
dt_y_pred = dt.predict(X_test)
dt_accuracy = accuracy_score(y_test, dt_y_pred)
print("Decision Tree Accuracy:", dt_accuracy)
# 随机森林分类器
rf = RandomForestClassifier(n_estimators=100, random_state=0)
rf.fit(X_train, y_train)
rf_y_pred = rf.predict(X_test)
rf_accuracy = accuracy_score(y_test, rf_y_pred)
print("Random Forest Accuracy:", rf_accuracy)
# KNN分类器
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
knn_y_pred = knn.predict(X_test)
knn_accuracy = accuracy_score(y_test, knn_y_pred)
print("KNN Accuracy:", knn_accuracy)
# SVM分类器
svm = svm.SVC(kernel='linear', C=1, random_state=0)
svm.fit(X_train, y_train)
svm_y_pred = svm.predict(X_test)
svm_accuracy = accuracy_score(y_test, svm_y_pred)
print("SVM Accuracy:", svm_accuracy)
# 识别新的鸢尾花
new_iris = [[5.1, 3.5, 1.4, 0.2]] # 假设这是一朵新的鸢尾花
new_iris_type = svm.predict(new_iris)
print("New Iris Type:", new_iris_type)
```
以上就是两个问题的代码示例,希望能对你有所帮助。
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