[机器学习|理论&实践] 机器学习在体育训练优化中的应用

# 以心率数据为例，使用 Python 中的 scikit-learn 库实现一个简单的回归模型

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

# 假设 X 是特征数据，y 是目标数据（比如运动员的表现得分）
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 使用线性回归模型进行训练
model = LinearRegression()
model.fit(X_train, y_train)

# 在测试集上进行预测
y_pred = model.predict(X_test)

# 计算均方误差
mse = mean_squared_error(y_test, y_pred)
print(f"均方误差：{mse}")

# 使用 Python 中的 pandas 和 scikit-learn 库进行数据处理和模型训练

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

# 假设 data 是历史比赛数据，包含特征和目标变量（得分）
data = pd.read_csv("basketball_data.csv")

# 特征选择
features = data[['AveragePoints', 'FieldGoalPercentage', 'MinutesPlayed']]

# 目标变量
target = data['Points']

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)

# 使用线性回归模型进行训练
model = LinearRegression()
model.fit(X_train, y_train)

# 在测试集上进行预测
y_pred = model.predict(X_test)

# 计算均方误差
mse = mean_squared_error(y_test, y_pred)
print(f"均方误差：{mse}")

# 假设 data 包含运动学数据、训练强度和生理指标
injury_data = pd.read_csv("injury_data.csv")

# 特征选择
injury_features = injury_data[['RunningSpeed', 'TrainingIntensity', 'PhysiologicalIndex']]

# 使用二分类模型（如逻辑回归）进行训练，目标变量为是否受伤
injury_target = injury_data['InjuryStatus']

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(injury_features, injury_target, test_size=0.2, random_state=42)

# 使用逻辑回归模型进行训练
injury_model = LogisticRegression()
injury_model.fit(X_train, y_train)

# 在测试集上进行预测
injury_pred = injury_model.predict(X_test)

# 计算准确率等评价指标
accuracy = accuracy_score(y_test, injury_pred)
print(f"准确率：{accuracy}")

# 假设 opponent_data 包含对手团队的比赛数据
opponent_data = pd.read_csv("opponent_data.csv")

# 特征选择
opponent_features = opponent_data[['AveragePointsAllowed', 'OpponentFieldGoalPercentage', 'DefensiveRating']]

# 使用分类模型进行训练，目标变量为对手团队的战术类型
opponent_target = opponent_data['TacticsType']

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(opponent_features, opponent_target, test_size=0.2, random_state=42)

# 使用决策树模型进行训练
tactics_model = DecisionTreeClassifier()
tactics_model.fit(X_train, y_train)

# 在测试集上进行预测
tactics_pred = tactics_model.predict(X_test)

# 计算准确率等评价指标
accuracy = accuracy_score(y_test, tactics_pred)
print(f"准确率：{accuracy}")