KNN分类器Python

Question

我目前正在使用scikit学习模块，以帮助解决犯罪预测问题。我有一个问题批次编码整个Dataframe与knn.predict方法。

如何用knn.predict()方法对我的Dataframe的整个两列进行批处理，以便将输出存储在另一个Dataframe中？

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

knn_df = pd.read_csv("/Users/helenapunset/Desktop/knn_dataframe.csv")

# x is the set of features 
x = knn_df[['latitude', 'longitude']]

# y is the target variable 
y = knn_df['Class']

# train and test data 
x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=0)

from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors = 5)

# training the data 
knn.fit(x_train,y_train)

# test score was approximately 69% 
knn.score(x_test,y_test)

# this is predicted to be a safe zone 
crime_prediction = knn.predict([[25.787882, -80.358427]])
print(crime_prediction)

在代码的最后一行中，我从标记为knn_df的Dataframe中添加了我使用的两个特性，即纬度和经度。但是，这是我一直在搜索的文档中关于简化整个Dataframe的knn预测的过程的一个单一点，并且似乎找不到一个方法来做到这一点。在某种程度上，是否有可能为此使用for循环？

Answer 1

让要预测的新集合是'knn_df_predict‘。假设列名相同，请尝试以下代码行：

x_new = knn_df_predict[['latitude', 'longitude']] #formating features
crime_prediction = knn.predict(x_new) #predicting for the new set
knn_df_predict['prediction'] = crime_prediction #Adding the prediction to dataframe