小编kan*_*aba的帖子

我正在尝试将"FreeSec"列的值设置True为我的pandas数据帧的已过滤行.这是代码:

data[data["Brand"].isin(group_clients)].FreeSec = True

但是,当我检查它们仍然设置为的值时False.

>>> data[data["Brand"].isin(group_clients)].FreeSec

12     False
163    False
164    False
165    False
166    False
167    False
168    False
169    False

我在这里错过了什么？

通过将PCA添加到算法中,我正在努力提高kaggle数字识别教程的%96.5 SKlearn kNN预测分数,但基于PCA输出的新kNN预测非常可怕,如23%.

下面是完整的代码,如果你指出我错在哪里,我感激不尽.

import pandas as pd
import numpy as np
import pylab as pl
import os as os
from sklearn import metrics
%pylab inline
os.chdir("/users/******/desktop/python")

traindata=pd.read_csv("train.csv")
traindata=np.array(traindata)
traindata=traindata.astype(float)
X,y=traindata[:,1:],traindata[:,0]

from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test= train_test_split(X,y,test_size=0.25, random_state=33)

#scale & PCA train data
from sklearn import preprocessing
from sklearn.decomposition import PCA
X_train_scaled = preprocessing.scale(X_train)
estimator = PCA(n_components=350)
X_train_pca = estimator.fit_transform(X_train_scaled)

# sum(estimator.explained_variance_ratio_) = 0.96

from sklearn.neighbors import KNeighborsClassifier
neigh = KNeighborsClassifier(n_neighbors=6)
neigh.fit(X_train_pca,y_train)

# scale & PCA test …