From 01f0633354702553607b2de5217cc51f40690068 Mon Sep 17 00:00:00 2001 From: 3286138832 <39480552+3286138832@users.noreply.github.com> Date: Fri, 25 May 2018 00:00:29 +0800 Subject: [PATCH] =?UTF-8?q?=E5=8F=82=E6=95=B0=E8=B0=83=E4=BC=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 参数调优：n_components=0.8，whiten=False score可以达到0.97300 --- src/python/getting-started/digit-recognizer/knn-python3.6.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/python/getting-started/digit-recognizer/knn-python3.6.py b/src/python/getting-started/digit-recognizer/knn-python3.6.py index b9a2261..cc9e1e8 100644 --- a/src/python/getting-started/digit-recognizer/knn-python3.6.py +++ b/src/python/getting-started/digit-recognizer/knn-python3.6.py @@ -60,7 +60,7 @@ def dRPCA(x_train, x_test, COMPONENT_NUM): 0 < n_components < 1 n_components=0.99 设置阈值总方差占比 ''' - pca = PCA(n_components=COMPONENT_NUM, whiten=True) + pca = PCA(n_components=COMPONENT_NUM, whiten=False) pca.fit(trainData) # Fit the model with X pcaTrainData = pca.transform(trainData) # Fit the model with X and 在X上完成降维. pcaTestData = pca.transform(testData) # Fit the model with X and 在X上完成降维. @@ -85,7 +85,7 @@ def dRecognition_knn(): print('load data time used:%f' % (stop_time_l - start_time)) # 降维处理 - trainData, testData = dRPCA(trainData, testData, 35) + trainData, testData = dRPCA(trainData, testData, 0.8) # 模型训练 knnClf = knnClassify(trainData, trainLabel)