机器学习模型得分字典的各预处理类别平均值计算及TypeError报错解决

嘿，我一眼就看出你遇到的TypeError问题根源啦——你的字典里所有模型得分都是字符串类型，而mean()函数只能处理数值（比如int或float），没法直接把字符串当作数字来计算平均值，这就是报错的核心原因。

修正思路 #

核心解决步骤就是：把每个字符串格式的得分转换成浮点型（float），然后再计算均值。我帮你优化了代码，不仅修复了错误，还加上了筛选最高平均得分预处理方法的逻辑，正好满足你的需求。

完整修正代码 #

from statistics import mean

# 你的原始得分字典
model_scores_for_datasets = {
    "Unprocessed": {"Logistic Regression": "0.967", "Support Vector Machine": "0.967", "Decision Tree": "0.933", "Random Forest": "0.933", "LinearDiscriminant": "1.000", "K-Nearest Neighbour": "1.000", "Naive Bayes": "0.967", "XGBoost": "0.933"},
    "Standardisation": {"Logistic Regression": "0.933", "Support Vector Machine": "0.967", "Decision Tree": "0.933", "Random Forest": "0.967", "LinearDiscriminant": "0.967", "K-Nearest Neighbour": "0.967", "Naive Bayes": "0.967", "XGBoost": "0.933"},
    "Normalisation": {"Logistic Regression": "0.967", "Support Vector Machine": "0.967", "Decision Tree": "0.933", "Random Forest": "0.967", "LinearDiscriminant": "0.967", "K-Nearest Neighbour": "0.967", "Naive Bayes": "0.967", "XGBoost": "0.933"},
    "Rescale": {"Logistic Regression": "0.967", "Support Vector Machine": "0.967", "Decision Tree": "0.933", "Random Forest": "0.933", "LinearDiscriminant": "0.967", "K-Nearest Neighbour": "0.967", "Naive Bayes": "0.967", "XGBoost": "0.933"}
}

# 存储每个预处理类别的平均得分
preprocessing_avg_scores = {}

# 遍历计算每个类别的均值
for preprocessing_type, model_scores in model_scores_for_datasets.items():
    # 将字符串得分转换为浮点型数值
    numeric_scores = [float(score) for score in model_scores.values()]
    # 计算均值并保留三位小数
    avg_score = mean(numeric_scores)
    preprocessing_avg_scores[preprocessing_type] = round(avg_score, 3)
    print(f"Average for {preprocessing_type} is {avg_score:.3f}")

# 筛选出平均得分最高的预处理方法
best_preprocessing = max(preprocessing_avg_scores.items(), key=lambda x: x[1])
print(f"\n✅ The best preprocessing method is '{best_preprocessing[0]}' with an average score of {best_preprocessing[1]:.3f}")

关键细节说明 #

• 类型转换：用列表推导式[float(score) for score in model_scores.values()]批量把字符串转成浮点型，这是解决报错的核心步骤
• 均值计算：使用Python标准库statistics里的mean()函数，如果你习惯用NumPy，也可以替换成np.mean(numeric_scores)（记得先导入numpy）
• 最优方法筛选：通过max()函数结合lambda表达式，直接从平均得分字典里找出得分最高的预处理类别

内容的提问来源于stack exchange，提问作者Francesca C