题解|根据特征阈值划分数据集

根据特征阈值划分数据集（Feature Thresholding）是一种常用的数据处理方法，用于将数据集分为两部分，一部分满足特征阈值，另一部分不满足特征阈值。

本题的关键是，要知道python中数值类型和字符串类型是不同的，不能直接比较，需要使用isinstance函数判断阈值类型，并且常用的数值类型也就int和float两种。

标准代码如下

def divide_on_feature(X, feature_i, threshold):
    # Define the split function based on the threshold type
    split_func = None
    if isinstance(threshold, int) or isinstance(threshold, float):
        # For numeric threshold, check if feature value is greater than or equal to the threshold
        split_func = lambda sample: sample[feature_i] >= threshold
    else:
        # For non-numeric threshold, check if feature value is equal to the threshold
        split_func = lambda sample: sample[feature_i] == threshold

    # Create two subsets based on the split function
    X_1 = np.array([sample for sample in X if split_func(sample)])
    X_2 = np.array([sample for sample in X if not split_func(sample)])

    # Return the two subsets
    return [X_1, X_2]