TestDome 数据科学：没有得到正确答案

Question

我尝试从 TestDome 回答这个问题并得到 250877.19298245612 而不是建议的 250000 。请告诉我出了什么问题。谢谢

import numpy as np
from sklearn import linear_model

class MarketingCosts:

    # param marketing_expenditure list. Expenditure for each previous campaign.
    # param units_sold list. The number of units sold for each previous campaign.
    # param desired_units_sold int. Target number of units to sell in the new campaign.
    # returns float. Required amount of money to be invested.
    @staticmethod
    def desired_marketing_expenditure(marketing_expenditure, units_sold, desired_units_sold):
        X = [[i] for i in units_sold]
        reg = linear_model.LinearRegression()
        reg.fit(X, marketing_expenditure)
        return float(reg.predict(desired_units_sold))

#For example, with the parameters below the function should return 250000.0.
print(MarketingCosts.desired_marketing_expenditure(
    [300000, 200000, 400000, 300000, 100000],
    [60000, 50000, 90000, 80000, 30000],
    60000))

Answer 1

我认为这就是解决方案，因为我们搜索从 y 预测 X，并且这个问题中的标签是units_sold。

import numpy as np
from sklearn import linear_model

class MarketingCosts:

    # param marketing_expenditure list. Expenditure for each previous campaign.
    # param units_sold list. The number of units sold for each previous campaign.
    # param desired_units_sold int. Target number of units to sell in the new campaign.
    # returns float. Required amount of money to be invested.
    @staticmethod
    def desired_marketing_expenditure(marketing_expenditure, units_sold, desired_units_sold):
        marketing_expenditure = marketing_expenditure.reshape(-1, 1)
        units_sold = units_sold.reshape(-1, 1)
        reg = linear_model.LinearRegression()
        reg.fit(marketing_expenditure , units_sold)
        return (desired_units_sold - reg.intercept_)/reg.coef_

#For example, with the parameters below the function should return 250000.0.
print(MarketingCosts.desired_marketing_expenditure(
    [300000, 200000, 400000, 300000, 100000],
    [60000, 50000, 90000, 80000, 30000],
    60000))