我的数据集如下所示:
| "Consignor Code" | "Consignee Code" | "Origin" | "Destination" | "Carrier Code" |
|------------------|------------------|----------|---------------|----------------|
| "6402106844" | "66903717" | "DKCPH" | "CNPVG" | "6402746387" |
| "6402106844" | "66903717" | "DKCPH" | "CNPVG" | "6402746387" |
| "6402106844" | "6404814143" | "DKCPH" | "CNPVG" | "6402746387" |
| "6402107662" | "66974631" | "DKCPH" | "VNSGN" | "6402746393" |
| "6402107662" | "6404518090" | "DKCPH" | "THBKK" | "6402746393" |
| "6402107662" | "6404518090" | "DKBLL" | "THBKK" | "6402746393" |
| "6408507648" | "6403601344" | "DKCPH" | "USTPA" | "66565231" |
我正试图在此基础上构建我的第一个ML模型。为此,我正在使用scikit学习。这是我的代码:
#Import the dependencies
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import make_scorer, accuracy_score
from sklearn.model_selection import cross_val_score, train_test_split
from sklearn.externals import joblib
from sklearn import preprocessing
import pandas as pd
#Import the dataset (A CSV file)
dataset = pd.read_csv('shipments.csv', header=0, skip_blank_lines=True)
#Drop any rows containing NaN values
dataset.dropna(subset=['Consignor Code', 'Consignee Code',
'Origin', 'Destination', 'Carrier Code'], inplace=True)
#Convert the numeric only cells to strings
dataset['Consignor Code'] = dataset['Consignor Code'].astype('int64')
dataset['Consignee Code'] = dataset['Consignee Code'].astype('int64')
dataset['Carrier Code'] = dataset['Carrier Code'].astype('int64')
#Define our target (What we want to be able to predict)
target = dataset.pop('Destination')
#Convert all our data to numeric values, so we can use the .fit function.
#For that, we use LabelEncoder
le = preprocessing.LabelEncoder()
target = le.fit_transform(list(target))
dataset['Origin'] = le.fit_transform(list(dataset['Origin']))
dataset['Consignor Code'] = le.fit_transform(list(dataset['Consignor Code']))
dataset['Consignee Code'] = le.fit_transform(list(dataset['Consignee Code']))
dataset['Carrier Code'] = le.fit_transform(list(dataset['Carrier Code']))
#Prepare the dataset.
X_train, X_test, y_train, y_test = train_test_split(
dataset, target, test_size=0.3, random_state=0)
#Prepare the model and .fit it.
model = RandomForestClassifier()
model.fit(X_train, y_train)
#Make a prediction on the test set.
predictions = model.predict(X_test)
#Print the accuracy score.
print("Accuracy score: {}".format(accuracy_score(y_test, predictions)))
现在,上面的代码返回:
Accuracy score: 0.7172413793103448
现在我的问题可能很愚蠢——但我如何使用我的model
来实际展示它对新数据的预测呢
考虑下面的<强>新的输入< /强>,并且我希望它预测^ {< CD2>}:
"6408507648","6403601344","DKCPH","","66565231"
如何用这些数据查询我的模型并得到预测的Destination
这里有一个包含预测的完整工作示例。最重要的部分是为每个特征定义不同的标签编码器,以便您可以使用相同的编码来拟合新数据,否则您将遇到错误(现在可能会显示错误,但在计算精度时您会注意到):
最后,您的树预测:
这里有一些快速的例子来说明这一点。在实践中我不会这样做,可能会有一些错误。例如,我认为如果测试集中存在看不见的类,那么这将失败
这里的关键概念是为您的功能使用单独的编码器,因为这些编码器对象记住如何对该功能进行编码。这是在
fit
阶段完成的。然后,您需要对任何新数据调用transform
,以正确编码该数据相关问题 更多 >
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