我们应该如何解释H2O预测函数的结果? [英] How should we interpret the results of the H2O predict function?
问题描述
我已经训练并存储了随机森林二进制分类模型.现在，我正在尝试使用此模型来模拟处理新的(样本外)数据.我的Python(Anaconda 3.6)代码是:
I have trained and stored a random forest binary classification model. Now I'm trying to simulate processing new (out-of-sample) data with this model. My Python (Anaconda 3.6) code is:
import h2o
import pandas as pd
import sys
localH2O = h2o.init(ip = "localhost", port = 54321, max_mem_size = "8G", nthreads = -1)
h2o.remove_all()
model_path = "C:/sm/BottleRockets/rf_model/DRF_model_python_1501621766843_28117";
model = h2o.load_model(model_path)
new_data = h2o.import_file(path="C:/sm/BottleRockets/new_data.csv")
print(new_data.head(10))
predict = model.predict(new_data) # predict returns a data frame
print(predict.describe())
predicted = predict[0,0]
probability = predict[0,2] # probability the prediction is a "1"
print('prediction: ', predicted, ', probability: ', probability)
运行此代码时，我得到:
When I run this code I get:
>>> import h2o
>>> import pandas as pd
>>> import sys
>>> localH2O = h2o.init(ip = "localhost", port = 54321, max_mem_size = "8G", nthreads = -1)
Checking whether there is an H2O instance running at http://localhost:54321. connected.
-------------------------- ------------------------------
H2O cluster uptime: 22 hours 22 mins
H2O cluster version: 3.10.5.4
H2O cluster version age: 18 days
H2O cluster name: H2O_from_python_Charles_0fqq0c
H2O cluster total nodes: 1
H2O cluster free memory: 6.790 Gb
H2O cluster total cores: 8
H2O cluster allowed cores: 8
H2O cluster status: locked, healthy
H2O connection url: http://localhost:54321
H2O connection proxy:
H2O internal security: False
Python version: 3.6.1 final
-------------------------- ------------------------------
>>> h2o.remove_all()
>>> model_path = "C:/sm/BottleRockets/rf_model/DRF_model_python_1501621766843_28117";
>>> model = h2o.load_model(model_path)
>>> new_data = h2o.import_file(path="C:/sm/BottleRockets/new_data.csv")
Parse progress: |█████████████████████████████████████████████████████████| 100%
>>> print(new_data.head(10))
BoxRatio Thrust Velocity OnBalRun vwapGain
---------- -------- ---------- ---------- ----------
1.502 55.044 0.38 37 0.845
[1 row x 5 columns]
>>> predict = model.predict(new_data) # predict returns a data frame
drf prediction progress: |████████████████████████████████████████████████| 100%
>>> print(predict.describe())
Rows:1
Cols:3
predict p0 p1
------- --------- ------------------ -------------------
type enum real real
mins 0.8849431818181818 0.11505681818181818
mean 0.8849431818181818 0.11505681818181818
maxs 0.8849431818181818 0.11505681818181818
sigma 0.0 0.0
zeros 0 0
missing 0 0 0
0 1 0.8849431818181818 0.11505681818181818
None
>>> predicted = predict[0,0]
>>> probability = predict[0,2] # probability the prediction is a "1"
>>> print('prediction: ', predicted, ', probability: ', probability)
prediction: 1 , probability: 0.11505681818181818
>>>
我对预测"数据帧的内容感到困惑.请告诉我标记为"p0"和"p1"的列中的数字是什么意思.我希望它们是概率，并且正如您从我的代码可以看到的那样，我正在尝试获得预测的分类(0或1)以及该分类正确的可能性.我的代码可以正确执行此操作吗?
I am confused by the contents of the "predict" data frame. Please tell me what the numbers in the columns labeled "p0" and "p1" mean. I hope they are probabilities, and as you can see by my code, I am trying to get the predicted classification (0 or 1) and a probability that this classification is correct. Does my code correctly do that?
任何评论将不胜感激. 查尔斯
Any comments will be greatly appreciated. Charles
推荐答案
p0是选择0类的可能性(介于0和1之间).
p0 is the probability (between 0 and 1) that class 0 is chosen.
p1是选择1类的概率(介于0和1之间).
p1 is the probability (between 0 and 1) that class 1 is chosen.
要记住的是，预测"是通过将阈值应用于p1来进行的.根据您要减少误报还是误报来选择该阈值点.不只是0.5.
The thing to keep in mind is that the "prediction" is made by applying a threshold to p1. That threshold point is chosen depending on whether you want to reduce false positives or false negatives. It's not just 0.5.
为预测"选择的阈值为max-F1.但是您可以自己提取p1并以任意方式对其设置阈值.
The threshold chosen for "the prediction" is max-F1. But you can extract out p1 yourself and threshold it any way you like.
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