5

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 预测结果非常糟糕,比如 23%。

以下是完整代码,如果您指出我错了,我将不胜感激。

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 data
X_test_scaled=preprocessing.scale(X_test)
X_test_pca=estimator.fit_transform(X_test_scaled)

y_test_pred=neigh.predict(X_test_pca)
# print metrics.accuracy_score(y_test, y_test_pred) = 0.23
# print metrics.classification_report(y_test, y_test_pred)
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2 回答 2

19

当您处理测试数据时,您使用fit_transform(X_test)的实际上是在测试数据上重新计算另一个 PCA 转换。您应该使用transform(X_test),以便测试数据经历与训练数据相同的转换。

代码部分看起来像(感谢 ogrisel 的whiten提示):

estimator = PCA(n_components=350, whiten=True)
X_train_pca = estimator.fit_transform(X_train)
X_test_pca = estimator.transform(X_test)

试试看是否有帮助?

于 2014-01-24T11:42:38.350 回答
0

你必须:

  1. 在训练集上拟合和变换(使用 .fit_transfrom )
  2. 并且仅在您的测试集上进行转换(使用 .transform )。
于 2017-08-18T03:23:27.857 回答