Optimization terminated successfully. Current function value:0.458884 Iterations7 Accuracy of Group1:0.7368421052631579 Optimization terminated successfully. Current function value:0.103458 Iterations10 Accuracy
from __future__ import print_functionfrom sklearn import datasetsimport matplotlib.pyplot as pltimport matplotlib.cm as cmximport matplotlib.colors as colorsimport numpy as np%matplotlib inline def shuffle_data(X, y, seed=None):if seed:np.random.seed...
predictions=np.around(model.predict(x_test))accuracy=metric.accuracy_score(y_test,predictions)print("Accuracy of Group {}: {}".format(i+1,accuracy))---Optimization terminated successfully.Currentfunctionvalue:0.458884Iterations7AccuracyofGroup1:0.7368421052631579Optimization terminated successfully.Currentfunc...
1.python (1)PCA的Python实现: ##Python实现PCAimportnumpy as npdefpca(X,k):#k is the components you want#mean of each featuren_samples, n_features =X.shape mean=np.array([np.mean(X[:,i])foriinrange(n_features)])#normalizationnorm_X=X-mean#scatter matrixscatter_matrix=np.dot(np.t...
Help on function load_boston in module sklearn.datasets._base: load_boston(*, return_X_y=False) Load and return the boston house-prices dataset (regression). === === Samples total 506 #506条记录 Dimensionality 13 #13个自变量维度Features real, positive Targets real 5. - 50. #target指...
Current function value: 0.103458 Iterations 10 Accuracy of Group 2: 0.9707602339181286 最后,我们将了解如何在开始建模之前使用 PCA 来更深入地了解数据。它将让你了解预期的分类准确度。你还将对哪些特征具有预测性建立直觉。这可以让你在特征选择方面占据优势。 如上所述,这种方法并非万无一失。它应该与其他...
PCA python 实现 from __future__ import print_function from sklearn import datasets import matplotlib.pyplot as plt import as cmx import matplotlib.colors as colors import numpy as np # matplotlib inline def shuffle_data(X, y, seed=None):...
Python程序如下 下面是封装成function的块 可直接调用 传入你需要训练的数据集即可 注意数据集最好为 .xls 后缀 def PCA_x(train_file_name, test_file_name, num_name): train_data = pd.read_excel(train_file_name, sheet_name=num_name) # 导入训练数据 test_data = pd.read_excel(test_file_name,...
from__future__importprint_function fromsklearnimportdatasets importmatplotlib.pyplot as plt importmatplotlib.cm as cmx importmatplotlib.colors as colors importnumpy as np # matplotlib inline defshuffle_data(X, y, seed=None): ifseed: np.random.seed(seed) ...
Python程序如下 下面是封装成function的块 可直接调用 传入你需要训练的数据集即可 注意数据集最好为 .xls 后缀 def PCA_x(train_file_name, test_file_name, num_name): train_data = pd.read_excel(train_file_name, sheet_name=num_name) # 导入训练数据 ...