Scikit-learn通關秘籍：從鳶尾花分類到房價預測

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決策樹/SVM/KNN算法對比 × 模型評估指標解析
讀者收獲：掌握經典機器學習全流程

當80%的機器學習問題可用Scikit-learn解決，掌握其核心流程將成為你的核心競爭力。本文通過對比實驗揭示算法本質，帶你一站式打通機器學習任督二脈。

一、Scikit-learn全景圖：3大核心模塊解析

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1.1 算法選擇矩陣

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1.2 環境極速配置

# 創建專用環境  
conda create -n sklearn_env python=3.10  
conda activate sklearn_env  # 安裝核心庫  
pip install numpy pandas matplotlib seaborn scikit-learn  # 驗證安裝  
import sklearn  
print(f"Scikit-learn version: {sklearn.__version__}")

二、分類實戰：鳶尾花識別

2.1 數據探索與預處理

from sklearn.datasets import load_iris  
import pandas as pd  # 加載數據集  
iris = load_iris()  
df = pd.DataFrame(iris.data, columns=iris.feature_names)  
df['target'] = iris.target  # 數據概覽  
print(f"樣本數: {df.shape[0]}")  
print(f"特征數: {df.shape[1]-1}")  
print(f"類別分布:\n{df['target'].value_counts()}")  # 可視化分析  
import seaborn as sns  
sns.pairplot(df, hue='target', palette='viridis')

2.2 三大分類器對比實驗

from sklearn.model_selection import train_test_split  
from sklearn.tree import DecisionTreeClassifier  
from sklearn.svm import SVC  
from sklearn.neighbors import KNeighborsClassifier  # 劃分數據集  
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)  # 初始化分類器  
models = {  "決策樹": DecisionTreeClassifier(max_depth=3),  "SVM": SVC(kernel='rbf', probability=True),  "KNN": KNeighborsClassifier(n_neighbors=5)  
}  # 訓練與評估  
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 分類結果可視化

import matplotlib.pyplot as plt  
from sklearn.metrics import confusion_matrix  # 繪制混淆矩陣  
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} 混淆矩陣")  
plt.show()

三、回歸實戰：波士頓房價預測

3.1 數據解析與特征工程

from sklearn.datasets import fetch_openml  # 加載數據集  
boston = fetch_openml(name='boston', version=1)  
df = pd.DataFrame(boston.data, columns=boston.feature_names)  
df['PRICE'] = boston.target  # 關鍵特征分析  
corr = df.corr()['PRICE'].sort_values(ascending=False)  
print(f"與房價相關性最高的特征:\n{corr.head(5)}")  # 特征工程  
df['RM_LSTAT'] = df['RM'] / df['LSTAT']  # 創造新特征

3.2 回歸模型對比

from sklearn.linear_model import LinearRegression  
from sklearn.tree import DecisionTreeRegressor  
from sklearn.svm import SVR  # 劃分數據集  
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)  # 初始化回歸器  
regressors = {  "線性回歸": LinearRegression(),  "決策樹回歸": DecisionTreeRegressor(max_depth=5),  "支持向量回歸": SVR(kernel='rbf')  
}  # 訓練與預測  
predictions = {}  
for name, reg in regressors.items():  reg.fit(X_train, y_train)  pred = reg.predict(X_test)  predictions[name] = pred

3.3 回歸結果可視化

# 繪制預測值與真實值對比  
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('真實價格')  plt.ylabel('預測價格')  plt.title(f'{name} 預測效果')  
plt.tight_layout()

四、模型評估指標深度解析

4.1 分類指標四維分析

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鳶尾花分類評估實例：

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,  "準確率": accuracy_score(y_test, y_pred),  "精確率": 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)

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4.2 回歸指標三維對比

波士頓房價評估實例：

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)

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五、算法原理對比揭秘

5.1 決策樹：可解釋性之王

核心參數調優指南：

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：高維空間的分割大師

核函數選擇策略：
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5.3 KNN：簡單高效的惰性學習

距離度量對比：

distance_metrics = [  ('euclidean', '歐氏距離'),  ('manhattan', '曼哈頓距離'),  ('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} 準確率: {score:.4f}")

六、模型優化實戰技巧

6.1 特征工程：性能提升關鍵

波士頓房價特征優化：

from sklearn.preprocessing import PolynomialFeatures  # 創建多項式特征  
poly = PolynomialFeatures(degree=2, include_bias=False)  
X_poly = poly.fit_transform(X)  # 新特征訓練  
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 交叉驗證：防止過擬合

from sklearn.model_selection import cross_val_score  # 5折交叉驗證  
scores = cross_val_score(  SVC(),  X, y,  cv=5,  scoring='accuracy'  
)  
print(f"平均準確率: {scores.mean():.4f} (±{scores.std():.4f})")

6.3 網格搜索：自動化調參

from sklearn.model_selection import GridSearchCV  # 定義參數網格  
param_grid = {  'C': [0.1, 1, 10, 100],  'gamma': [1, 0.1, 0.01, 0.001],  'kernel': ['rbf', 'linear']  
}  # 執行搜索  
grid = GridSearchCV(SVC(), param_grid, refit=True, verbose=3)  
grid.fit(X_train, y_train)  
print(f"最優參數: {grid.best_params_}")

七、工業級部署方案

7.1 模型持久化

import joblib  # 保存模型  
joblib.dump(best_model, 'iris_classifier.pkl')  # 加載模型  
clf = joblib.load('iris_classifier.pkl')  # 在線預測  
new_data = [[5.1, 3.5, 1.4, 0.2]]  
prediction = clf.predict(new_data)  
print(f"預測類別: {iris.target_names[prediction[0]]}")

7.2 構建預測API

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 性能監控儀表盤

from sklearn.metrics import plot_roc_curve, plot_precision_recall_curve  # 分類性能可視化  
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：常見錯誤解決方案

8.1 數據預處理陷阱

問題：測試集出現未知類別
解決方案：

from sklearn.preprocessing import OneHotEncoder  # 訓練階段  
encoder = OneHotEncoder(handle_unknown='ignore')  
encoder.fit(X_train_categorical)  # 測試階段自動忽略未知類別  
X_test_encoded = encoder.transform(X_test_categorical)

8.2 特征尺度問題

癥狀：SVM/KNN性能異常
處方：

from sklearn.preprocessing import StandardScaler  scaler = StandardScaler()  
X_train_scaled = scaler.fit_transform(X_train)  
X_test_scaled = scaler.transform(X_test)  # 注意：只變換不擬合

8.3 樣本不均衡處理

解決方案對比：
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結語：機器學習工程師的成長之路

當你在Scikit-learn中完整實現從數據加載到模型部署的全流程，已超越70%的入門者。但真正的進階之路剛剛開始。

下一步行動指南：

# 1. 復現經典論文算法  
from sklearn.linear_model import LogisticRegression  
model = LogisticRegression(penalty='l1', solver='liblinear')  # 2. 參加Kaggle競賽  
from kaggle import api  
api.competitions_list(search='getting started')  # 3. 構建個人項目組合  
projects = [  {"name": "鳶尾花分類器", "type": "分類", "accuracy": 0.97},  {"name": "房價預測", "type": "回歸", "R2": 0.85}  
]

記住：在機器學習領域，理論認知的深度=代碼實踐的厚度。現在運行你的第一個完整流程，讓Scikit-learn成為你AI旅程中最可靠的伙伴。

附錄：Scikit-learn速查表

任務類型	導入路徑	核心參數
分類	`from sklearn.ensemble import RandomForestClassifier`	`n_estimators, max_depth`
回歸	`from sklearn.linear_model import LinearRegression`	`fit_intercept, normalize`
聚類	`from sklearn.cluster import KMeans`	`n_clusters, init`
降維	`from sklearn.decomposition import PCA`	`n_components`
模型選擇	`from sklearn.model_selection import GridSearchCV`	`param_grid, cv`
數據預處理	`from sklearn.preprocessing import StandardScaler`	`with_mean, with_std`