机器学习入门的重要性
机器学习是人工智能的核心技术，通过算法让计算机从数据中学习模式，做出预测和决策。scikit-learn作为Python最流行的机器学习库，提供了丰富的算法和工具，是学习机器学习的理想起点。
scikit-learn基础
1. 基本分类
from sklearn.datasets import load_iris, make_classification
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
import numpy as np
import pandas as pd
from datetime import datetime
def basic_classification():
"""基本分类示例"""
print("=== 基本分类示例 ===")
# 加载鸢尾花数据集
iris = load_iris()
X, y = iris.data, iris.target
print(f"数据集形状: {X.shape}")
print(f"特征名称: {iris.feature_names}")
print(f"目标类别: {iris.target_names}")
# 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=42, stratify=y
)
print(f"训练集大小: {X_train.shape}")
print(f"测试集大小: {X_test.shape}")
# 训练随机森林分类器
rf_clf = RandomForestClassifier(n_estimators=100, random_state=42)
rf_clf.fit(X_train, y_train)
# 预测和评估
y_pred = rf_clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"\\n随机森林准确率: {accuracy:.4f}")
print("\\n分类报告:")
print(classification_report(y_test, y_pred, target_names=iris.target_names))
# 特征重要性
feature_importance = rf_clf.feature_importances_
print("\\n特征重要性:")
for feature, importance in zip(iris.feature_names, feature_importance):
print(f"{feature}: {importance:.4f}")
basic_classification()
2. 多种分类算法对比
def compare_classifiers():
"""多种分类算法对比"""
print("=== 多种分类算法对比 ===")
# 创建合成数据集
X, y = make_classification(
n_samples=1000, n_features=20, n_informative=15,
n_redundant=5, n_classes=3, random_state=42
)
# 划分数据集
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.3, random_state=42
)
# 定义分类器
classifiers = {
'随机森林': RandomForestClassifier(n_estimators=100, random_state=42),
'逻辑回归': LogisticRegression(random_state=42, max_iter=1000),
'支持向量机': SVC(random_state=42)
}
results = {}
for name, clf in classifiers.items():
# 训练模型
clf.fit(X_train, y_train)
# 预测
y_pred = clf.predict(X_test)
# 评估
accuracy = accuracy_score(y_test, y_pred)
# 交叉验证
cv_scores = cross_val_score(clf, X_train, y_train, cv=5)
results[name] = {
'accuracy': accuracy,
'cv_mean': cv_scores.mean(),
'cv_std': cv_scores.std()
}
print(f"\\n{name}:")
print(f" 测试准确率: {accuracy:.4f}")
print(f" 交叉验证: {cv_scores.mean():.4f} (+/- {cv_scores.std() * 2:.4f})")
# 找出最佳模型
best_model = max(results.items(), key=lambda x: x[1]['accuracy'])
print(f"\\n最佳模型: {best_model[0]} (准确率: {best_model[1]['accuracy']:.4f})")
compare_classifiers()
回归算法
1. 基本回归
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression, Ridge, Lasso
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error
def basic_regression():
"""基本回归示例"""
print("=== 基本回归示例 ===")
# 创建回归数据集
X, y = make_regression(
n_samples=1000, n_features=10, noise=0.1, random_state=42
)
# 划分数据集
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.3, random_state=42
)
# 训练线性回归模型
lr = LinearRegression()
lr.fit(X_train, y_train)
# 预测
y_pred = lr.predict(X_test)
# 评估指标
mse = mean_squared_error(y_test, y_pred)
rmse = np.sqrt(mse)
mae = mean_absolute_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print(f"均方误差 (MSE): {mse:.4f}")
print(f"均方根误差 (RMSE): {rmse:.4f}")
print(f"平均绝对误差 (MAE): {mae:.4f}")
print(f"决定系数 (R²): {r2:.4f}")
# 特征系数
print("\\n特征系数:")
for i, coef in enumerate(lr.coef_):
print(f"特征 {i}: {coef:.4f}")
print(f"截距: {lr.intercept_:.4f}")
basic_regression()
实际应用案例
1. 房价预测
def house_price_prediction():
"""房价预测案例"""
print("=== 房价预测案例 ===")
# 创建房价数据
np.random.seed(42)
n_samples = 1000
# 特征：面积、房间数、楼层、建造年份
area = np.random.normal(120, 30, n_samples)
rooms = np.random.randint(1, 6, n_samples)
floor = np.random.randint(1, 21, n_samples)
year = np.random.randint(1990, 2020, n_samples)
# 目标：房价（基于特征计算）
price = (area * 100 + rooms * 20000 + floor * 1000 +
(2020 - year) * 500 + np.random.normal(0, 50000, n_samples))
price = np.maximum(price, 100000) # 确保价格为正
# 创建DataFrame
df = pd.DataFrame({
'area': area,
'rooms': rooms,
'floor': floor,
'year': year,
'price': price
})
print("数据概览:")
print(df.head())
print(f"\\n数据形状: {df.shape}")
# 准备特征和目标
X = df[['area', 'rooms', 'floor', 'year']]
y = df['price']
# 划分数据集
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.3, random_state=42
)
# 训练模型
rf_reg = RandomForestRegressor(n_estimators=100, random_state=42)
rf_reg.fit(X_train, y_train)
# 预测
y_pred = rf_reg.predict(X_test)
# 评估
mse = mean_squared_error(y_test, y_pred)
rmse = np.sqrt(mse)
r2 = r2_score(y_test, y_pred)
print(f"\\n模型性能:")
print(f"均方根误差: {rmse:.2f}")
print(f"决定系数: {r2:.4f}")
# 特征重要性
feature_importance = rf_reg.feature_importances_
print(f"\\n特征重要性:")
for feature, importance in zip(X.columns, feature_importance):
print(f"{feature}: {importance:.4f}")
# 预测示例
sample_house = [[150, 3, 10, 2015]] # 150平米，3室，10楼，2015年
predicted_price = rf_reg.predict(sample_house)[0]
print(f"\\n预测示例:")
print(f"150平米，3室，10楼，2015年建造 -> 预测价格: {predicted_price:.2f}元")
house_price_prediction()
总结
掌握scikit-learn机器学习是数据科学的核心：
- 基础算法：理解分类、回归等基本算法
- 模型评估：掌握各种评估指标和交叉验证
- 特征工程：学会特征选择、预处理和缩放
- 实际应用：在房价预测、客户分类等场景中的应用
- 最佳实践：遵循机器学习的最佳实践
- 持续学习：不断探索新的算法和技术
通过系统学习这些概念，你将能够构建出有效的机器学习模型，解决实际的数据科学问题。
转载请注明：周志洋的博客 » Python实用技巧-机器学习入门
