根据决策树算法生成的模型进行预测

Question

我一直在尝试做出一个预测，该预测由我使用决策树算法创建的模型中的 DataFrame 组成。

我的模型得分为 0.96。然后，我尝试使用该模型对留下但出错的 DataFrame 人进行预测。目标是根据留下的 DataFrame 预测未来将离开公司的人。

如何实现这个目标？

所以我做的是：

1. 从我的 github 读取 DF 并将它们分成离开和未离开的人

df = pd.read_csv('https://raw.githubusercontent.com/bhaskoro-muthohar/DataScienceLearning/master/HR_comma_sep.csv')

leftdf = df[df['left']==1]
notleftdf =df[df['left']==0]

1. 为模型生成准备数据

df.salary = df.salary.map({'low':0,'medium':1,'high':2})
df.salary

X = df.drop(['left','sales'],axis=1)
y = df['left']

1. 拆分训练集和测试集

import numpy as np
from sklearn.model_selection import train_test_split


#splitting the train and test sets
X_train, X_test, y_train, y_test= train_test_split(X,y,random_state=0, stratify=y)

4. 训练它

from sklearn import tree
clftree = tree.DecisionTreeClassifier(max_depth=3)
clftree.fit(X_train,y_train)

5. 评估模型

y_pred = clftree.predict(X_test)
print("Test set prediction:\n {}".format(y_pred))
print("Test set score: {:.2f}".format(clftree.score(X_test, y_test)))

结果是

测试集分数：0.96

6. 然后我尝试使用尚未离开公司的人的 DataFrame 进行预测

X_new = notleftdf.drop(['left','sales'],axis=1)

#Map salary to 0,1,2
X_new.salary = X_new.salary.map({'low':0,'medium':1,'high':2})
X_new.salary

prediction_will_left = clftree.predict(X_new)
print("Prediction: {}".format(prediction_will_left))
print("Predicted target name: {}".format(
    notleftdf['left'][prediction_will_left]
))

我得到的错误是：

KeyError: "None of [Int64Index([0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n            ...\n            0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n           dtype='int64', length=11428)] are in the [index]"

如何解决？

PS：完整的脚本链接是here

Answer 1

也许您正在寻找类似的东西。 （将the data file 下载到同一目录后的自包含脚本。）

from sklearn import tree
from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd


def process_df_for_ml(df):
    """
    Process a dataframe for model training/prediction use.

    Returns X/y tensors.
    """

    df = df.copy()
    # Map salary to 0,1,2
    df.salary = df.salary.map({"low": 0, "medium": 1, "high": 2})
    # dropping left and sales X for the df, y for the left
    X = df.drop(["left", "sales"], axis=1)
    y = df["left"]
    return (X, y)

# Read and reindex CSV.
df = pd.read_csv("HR_comma_sep.csv")
df = df.reindex()

# Train a decision tree.
X, y = process_df_for_ml(df)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, stratify=y)
clftree = tree.DecisionTreeClassifier(max_depth=3)
clftree.fit(X_train, y_train)

# Test the decision tree on people who haven't left yet.
notleftdf = df[df["left"] == 0].copy()
X, y = process_df_for_ml(notleftdf)
# Plug in a new column with ones and zeroes from the prediction.
notleftdf["will_leave"] = clftree.predict(X)
# Print those with the will-leave flag on.
print(notleftdf[notleftdf["will_leave"] == 1])