做一个身高预测和典型判定的编程码男女都要。

要创建一个身高预测和性别分类的程序，我们可以使用机器学习中的回归模型进行身高预测，同时利用简单的特征（如平均身高的差异）来进行性别分类。这里我将提供一个基础的Python代码示例，使用scikit-learn库。首先确保已经安装了必要的库：

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error, accuracy_score

# 假设你有一个包含身高（in cm）和性别的数据集
# 数据文件名假设为'study_data.csv'
data = pd.read_csv('study_data.csv')

# 提取特征和标签
X = data[['height']]  # 假设'height'列是身高
y_height = data['target_height']  # 假设'target_height'列是实际身高
y_gender = data['gender']  # 假设'gender'列是男性或女性

# 数据预处理
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# 划分训练集和测试集
X_train, X_test, y_height_train, y_height_test = train_test_split(X_scaled, y_height, test_size=0.2, random_state=42)
X_male, X_female, y_male, y_female = train_test_split(X[y_gender == 'male'], X[y_gender == 'female'], test_size=0.2, random_state=42)  # 分别对男女人群做划分

# 创建两个回归模型，分别针对男性和女性
model_male = LinearRegression()
model_female = LinearRegression()

# 训练模型
model_male.fit(X_male, y_male)
model_female.fit(X_female, y_female)

# 预测身高
y_height_pred = scaler.inverse_transform(model_male.predict(X)) if model_male else scaler.inverse_transform(model_female.predict(X))
y_male_pred = scaler.inverse_transform(model_male.predict(X_male))
y_female_pred = scaler.inverse_transform(model_female.predict(X_female))

# 评估身高预测性能
mse_height = mean_squared_error(y_height_test, y_height_pred)
accuracy_height = round(1 - mse_height / (max(y_height_test) - min(y_height_test)), 2) * 100  # 用R²代替准确性更好，但这里简化为百分比误差

# 性别分类
def classify_gender(height):
    if height > model_male.intercept_ + model_male.coef_[0] * X_male.mean()[0]:
        return 'Male'
    elif height < model_female.intercept_ + model_female.coef_[0] * X_female.mean()[0]:
        return 'Female'
    else:
        return 'Ambiguous'

# 示例分类
test_height = [175, 160]
predictions = [classify_gender(height) for height in test_height]

print(f"Height prediction MSE: {mse_height}")
print(f"Height prediction accuracy: {accuracy_height}%")
print("Gender predictions:", predictions)