点击 "AladdinEdu，同学们用得起的【H卡】算力平台"，H卡级别算力，按量计费，灵活弹性，顶级配置，学生专属优惠。
决策树/SVM/KNN算法对比 × 模型评估指标解析
读者收获：掌握经典机器学习全流程
当80%的机器学习问题可用Scikit-learn解决，掌握其核心流程将成为你的核心竞争力。本文通过对比实验揭示算法本质，带你一站式打通机器学习任督二脉。
一、Scikit-learn全景图：3大核心模块解析
1.1 算法选择矩阵
1.2 环境极速配置
bash
# 创建专用环境
conda create -n sklearn_env python=3.10
conda activate sklearn_env
# 安装核心库
pip install numpy pandas matplotlib seaborn scikit-learn
# 验证安装
import sklearn
print(f"Scikit-learn version: {sklearn.__version__}") 二、分类实战：鸢尾花识别
2.1 数据探索与预处理
python
from sklearn.datasets import load_iris
import pandas as pd
# 加载数据集
iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.target
# 数据概览
print(f"样本数: {df.shape[0]}")
print(f"特征数: {df.shape[1]-1}")
print(f"类别分布:\n{df['target'].value_counts()}")
# 可视化分析
import seaborn as sns
sns.pairplot(df, hue='target', palette='viridis') 2.2 三大分类器对比实验
python
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
# 划分数据集
X = df.drop(columns='target')
y = df['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 初始化分类器
models = {
"决策树": DecisionTreeClassifier(max_depth=3),
"SVM": SVC(kernel='rbf', probability=True),
"KNN": KNeighborsClassifier(n_neighbors=5)
}
# 训练与评估
results = {}
for name, model in models.items():
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
results[name] = y_pred 2.3 分类结果可视化
python
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
# 绘制混淆矩阵
fig, axes = plt.subplots(1, 3, figsize=(18, 5))
for i, (name, y_pred) in enumerate(results.items()):
cm = confusion_matrix(y_test, y_pred)
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', ax=axes[i])
axes[i].set_title(f"{name} 混淆矩阵")
plt.show() 三、回归实战：波士顿房价预测
3.1 数据解析与特征工程
python
from sklearn.datasets import fetch_openml
# 加载数据集
boston = fetch_openml(name='boston', version=1)
df = pd.DataFrame(boston.data, columns=boston.feature_names)
df['PRICE'] = boston.target
# 关键特征分析
corr = df.corr()['PRICE'].sort_values(ascending=False)
print(f"与房价相关性最高的特征:\n{corr.head(5)}")
# 特征工程
df['RM_LSTAT'] = df['RM'] / df['LSTAT'] # 创造新特征 3.2 回归模型对比
python
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.svm import SVR
# 划分数据集
X = df.drop(columns='PRICE')
y = df['PRICE']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 初始化回归器
regressors = {
"线性回归": LinearRegression(),
"决策树回归": DecisionTreeRegressor(max_depth=5),
"支持向量回归": SVR(kernel='rbf')
}
# 训练与预测
predictions = {}
for name, reg in regressors.items():
reg.fit(X_train, y_train)
pred = reg.predict(X_test)
predictions[name] = pred 3.3 回归结果可视化
python
# 绘制预测值与真实值对比
plt.figure(figsize=(15, 10))
for i, (name, pred) in enumerate(predictions.items(), 1):
plt.subplot(3, 1, i)
plt.scatter(y_test, pred, alpha=0.7)
plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--')
plt.xlabel('真实价格')
plt.ylabel('预测价格')
plt.title(f'{name} 预测效果')
plt.tight_layout() 四、模型评估指标深度解析
4.1 分类指标四维分析
鸢尾花分类评估实例：
python
from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score
metrics = []
for name, y_pred in results.items():
metrics.append({
"模型": name,
"准确率": accuracy_score(y_test, y_pred),
"精确率": precision_score(y_test, y_pred, average='macro'),
"召回率": recall_score(y_test, y_pred, average='macro'),
"F1": f1_score(y_test, y_pred, average='macro')
})
metrics_df = pd.DataFrame(metrics)
print(metrics_df) 
4.2 回归指标三维对比
波士顿房价评估实例：
python
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
reg_metrics = []
for name, pred in predictions.items():
reg_metrics.append({
"模型": name,
"MSE": mean_squared_error(y_test, pred),
"MAE": mean_absolute_error(y_test, pred),
"R²": r2_score(y_test, pred)
})
reg_metrics_df = pd.DataFrame(reg_metrics)
print(reg_metrics_df) 
五、算法原理对比揭秘
5.1 决策树：可解释性之王
核心参数调优指南：
python
params = {
'max_depth': [3, 5, 7, None],
'min_samples_split': [2, 5, 10],
'criterion': ['gini', 'entropy']
}
best_tree = GridSearchCV(
DecisionTreeClassifier(),
param_grid=params,
cv=5,
scoring='f1_macro'
)
best_tree.fit(X_train, y_train) 5.2 SVM：高维空间的分割大师
核函数选择策略：
5.3 KNN：简单高效的惰性学习
距离度量对比：
python
distance_metrics = [
('euclidean', '欧氏距离'),
('manhattan', '曼哈顿距离'),
('cosine', '余弦相似度')
]
for metric, name in distance_metrics:
knn = KNeighborsClassifier(n_neighbors=5, metric=metric)
knn.fit(X_train, y_train)
score = knn.score(X_test, y_test)
print(f"{name} 准确率: {score:.4f}") 六、模型优化实战技巧
6.1 特征工程：性能提升关键
波士顿房价特征优化：
python
from sklearn.preprocessing import PolynomialFeatures
# 创建多项式特征
poly = PolynomialFeatures(degree=2, include_bias=False)
X_poly = poly.fit_transform(X)
# 新特征训练
lr_poly = LinearRegression()
lr_poly.fit(X_train_poly, y_train)
r2 = lr_poly.score(X_test_poly, y_test)
print(f"R²提升: {reg_metrics_df.loc[0,'R²']:.2f} → {r2:.2f}") 6.2 交叉验证：防止过拟合
python
from sklearn.model_selection import cross_val_score
# 5折交叉验证
scores = cross_val_score(
SVC(),
X, y,
cv=5,
scoring='accuracy'
)
print(f"平均准确率: {scores.mean():.4f} (±{scores.std():.4f})") 6.3 网格搜索：自动化调参
python
from sklearn.model_selection import GridSearchCV
# 定义参数网格
param_grid = {
'C': [0.1, 1, 10, 100],
'gamma': [1, 0.1, 0.01, 0.001],
'kernel': ['rbf', 'linear']
}
# 执行搜索
grid = GridSearchCV(SVC(), param_grid, refit=True, verbose=3)
grid.fit(X_train, y_train)
print(f"最优参数: {grid.best_params_}") 七、工业级部署方案
7.1 模型持久化
python
import joblib
# 保存模型
joblib.dump(best_model, 'iris_classifier.pkl')
# 加载模型
clf = joblib.load('iris_classifier.pkl')
# 在线预测
new_data = [[5.1, 3.5, 1.4, 0.2]]
prediction = clf.predict(new_data)
print(f"预测类别: {iris.target_names[prediction[0]]}") 7.2 构建预测API
python
from flask import Flask, request, jsonify
app = Flask(__name__)
model = joblib.load('iris_classifier.pkl')
@app.route('/predict', methods=['POST'])
def predict():
data = request.get_json()
features = [data['sepal_length'], data['sepal_width'],
data['petal_length'], data['petal_width']]
prediction = model.predict([features])
return jsonify({'class': iris.target_names[prediction[0]]})
if __name__ == '__main__':
app.run(host='0.0.0.0', port=5000) 7.3 性能监控仪表盘
python
from sklearn.metrics import plot_roc_curve, plot_precision_recall_curve
# 分类性能可视化
fig, ax = plt.subplots(1, 2, figsize=(15, 6))
plot_roc_curve(model, X_test, y_test, ax=ax[0])
plot_precision_recall_curve(model, X_test, y_test, ax=ax[1]) 八、避坑指南：常见错误解决方案
8.1 数据预处理陷阱
问题 ：测试集出现未知类别
解决方案：
python
from sklearn.preprocessing import OneHotEncoder
# 训练阶段
encoder = OneHotEncoder(handle_unknown='ignore')
encoder.fit(X_train_categorical)
# 测试阶段自动忽略未知类别
X_test_encoded = encoder.transform(X_test_categorical) 8.2 特征尺度问题
症状 ：SVM/KNN性能异常
处方：
python
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test) # 注意：只变换不拟合 8.3 样本不均衡处理
解决方案对比 ：
结语：机器学习工程师的成长之路
当你在Scikit-learn中完整实现从数据加载到模型部署的全流程，已超越70%的入门者。但真正的进阶之路刚刚开始。
下一步行动指南：
python
# 1. 复现经典论文算法
from sklearn.linear_model import LogisticRegression
model = LogisticRegression(penalty='l1', solver='liblinear')
# 2. 参加Kaggle竞赛
from kaggle import api
api.competitions_list(search='getting started')
# 3. 构建个人项目组合
projects = [
{"name": "鸢尾花分类器", "type": "分类", "accuracy": 0.97},
{"name": "房价预测", "type": "回归", "R2": 0.85}
] 记住：在机器学习领域，理论认知的深度=代码实践的厚度。现在运行你的第一个完整流程，让Scikit-learn成为你AI旅程中最可靠的伙伴。
附录：Scikit-learn速查表
| 任务类型 | 导入路径 | 核心参数 |
|---|---|---|
| 分类 | from sklearn.ensemble import RandomForestClassifier |
n_estimators, max_depth |
| 回归 | from sklearn.linear_model import LinearRegression |
fit_intercept, normalize |
| 聚类 | from sklearn.cluster import KMeans |
n_clusters, init |
| 降维 | from sklearn.decomposition import PCA |
n_components |
| 模型选择 | from sklearn.model_selection import GridSearchCV |
param_grid, cv |
| 数据预处理 | from sklearn.preprocessing import StandardScaler |
with_mean, with_std |