聲明：內(nèi)容非原創(chuàng)，代碼來自葁sir

import numpy as np import pandas as pd import matplotlib.pyplot as plt %matplotlib inline # 導(dǎo)入數(shù)據(jù)集 seeds = pd.read_csv('data/seeds.csv',sep = '\t',header = None) seeds.head() 0123456701234

15.26	14.84	0.8710	5.763	3.312	2.221	5.220	Kama
14.88	14.57	0.8811	5.554	3.333	1.018	4.956	Kama
14.29	14.09	0.9050	5.291	3.337	2.699	4.825	Kama
13.84	13.94	0.8955	5.324	3.379	2.259	4.805	Kama
16.14	14.99	0.9034	5.658	3.562	1.355	5.175	Kama

# 觀察小麥有多少類 seeds[7].value_counts() Kama 70 Rosa 70 Canadian 70 Name: 7, dtype: int64 seeds[7].value_counts().plot(kind = 'bar') <AxesSubplot:>

# 或者用seaborn import seaborn as sns sns.set() # seaborn 常用圖像 # barplot() # scatterplot() # swanrmplot() # boxplot() # violinplot() # countplot() # pairplot() # heatmap() from sklearn.model_selection import train_test_split from sklearn.linear_model import Lasso,RidgeClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.preprocessing import MinMaxScaler,StandardScaler X = seeds.iloc[:,:7].copy() # X = seeds.values[:,:7].copy() # 但是這樣復(fù)制 numpy.ndarray X.shape (210, 7) X 012345601234...205206207208209

15.26	14.84	0.8710	5.763	3.312	2.221	5.220
14.88	14.57	0.8811	5.554	3.333	1.018	4.956
14.29	14.09	0.9050	5.291	3.337	2.699	4.825
13.84	13.94	0.8955	5.324	3.379	2.259	4.805
16.14	14.99	0.9034	5.658	3.562	1.355	5.175
...	...	...	...	...	...	...
12.19	13.20	0.8783	5.137	2.981	3.631	4.870
11.23	12.88	0.8511	5.140	2.795	4.325	5.003
13.20	13.66	0.8883	5.236	3.232	8.315	5.056
11.84	13.21	0.8521	5.175	2.836	3.598	5.044
12.30	13.34	0.8684	5.243	2.974	5.637	5.063

210 rows × 7 columns

y = seeds.iloc[:,-1].copy() # y = seeds.values[:,-1].copy() y.shape (210,) X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=1) # 封裝函數(shù)來進行knn試探性運算 def knn_score(k,X,y):# 構(gòu)造算法對象knn = KNeighborsClassifier(n_neighbors = k)scores = []train_scores = []for i in range(100):# 拆分X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=1)# 訓練knn.fit(X_train,y_train)# 評價模型scores.append(knn.score(X_test,y_test))# 經(jīng)驗評分train_scores.append(knn.score(X_train,y_train))return np.array(scores).mean(),np.array(train_scores).mean()# 調(diào)參 result_dict = {} k_list = [1,3,5,7,9,11] for k in k_list:score,train_score = knn_score(k,X,y)result_dict[k] = [score,train_score] result_dict {1: [0.9047619047619047, 1.0],3: [0.9047619047619047, 0.9642857142857139],5: [0.8571428571428572, 0.9285714285714287],7: [0.8571428571428572, 0.9345238095238096],9: [0.8809523809523812, 0.9226190476190478],11: [0.8809523809523812, 0.9226190476190478]} pd.DataFrame(result_dict).T 011357911

0.904762	1.000000
0.904762	0.964286
0.857143	0.928571
0.857143	0.934524
0.880952	0.922619
0.880952	0.922619

result = pd.DataFrame(result_dict).T.copy() result.columns = ['Test','Train'] result TestTrain1357911

0.904762	1.000000
0.904762	0.964286
0.857143	0.928571
0.857143	0.934524
0.880952	0.922619
0.880952	0.922619

result.plot() plt.xticks(k_list) plt.show()

進階版

# z-score (x-x.mean)/ x.std N(0,1) # MinMaxScaller (x-x.min)/(x.max-x.min) 0-1 # 異常值 空值 數(shù)據(jù)分布查看 X.shape (210, 7) # 查看統(tǒng)計學指標 X.describe().T countmeanstdmin25%50%75%max0123456

210.0	14.847524	2.909699	10.5900	12.27000	14.35500	17.305000	21.1800
210.0	14.559286	1.305959	12.4100	13.45000	14.32000	15.715000	17.2500
210.0	0.870999	0.023629	0.8081	0.85690	0.87345	0.887775	0.9183
210.0	5.628533	0.443063	4.8990	5.26225	5.52350	5.979750	6.6750
210.0	3.258605	0.377714	2.6300	2.94400	3.23700	3.561750	4.0330
210.0	3.700201	1.503557	0.7651	2.56150	3.59900	4.768750	8.4560
210.0	5.408071	0.491480	4.5190	5.04500	5.22300	5.877000	6.5500

def standard_X(X):X_copy = X.copy() # 拿數(shù)據(jù)for col_name in X_copy.columns: # 取列名col_data = X_copy[[col_name]] # 根據(jù)列名拿列數(shù)據(jù)，兩個方括號是因為要二維數(shù)組# fit_transformstand_data = StandardScaler().fit_transform(col_data.values) # 標準化X_copy[col_name] = stand_data # 將數(shù)據(jù)替換成標準化后的數(shù)據(jù)return X_copy standard_X(X).describe([0.01,0.25,0.5,0.75,0.99]).T # standard_X(X).describe([0.01,0.25,0.5,0.75,0.99]).T countmeanstdmin1%25%50%75%99%max0123456

210.0	-5.392512e-17	1.002389	-1.466714	-1.397504	-0.887955	-0.169674	0.846599	2.072913	2.181534
210.0	9.146123e-17	1.002389	-1.649686	-1.474607	-0.851433	-0.183664	0.887069	2.023505	2.065260
210.0	1.322091e-15	1.002389	-2.668236	-2.588824	-0.598079	0.103993	0.711677	1.678118	2.006586
210.0	-2.182910e-15	1.002389	-1.650501	-1.464372	-0.828682	-0.237628	0.794595	2.154459	2.367533
210.0	-2.030122e-16	1.002389	-1.668209	-1.634930	-0.834907	-0.057335	0.804496	1.936725	2.055112
210.0	-3.679596e-16	1.002389	-1.956769	-1.857934	-0.759148	-0.067469	0.712379	2.519905	3.170590
210.0	-1.337554e-16	1.002389	-1.813288	-1.633810	-0.740495	-0.377459	0.956394	2.130797	2.328998

查看數(shù)據(jù)分布

經(jīng)過對標準化數(shù)據(jù)describe查看99分位數(shù) 發(fā)現(xiàn)標簽為2和5的兩個列 有較大差距

stand_X = standard_X(X) for col_name in stand_X.columns:sns.distplot(stand_X[col_name])plt.title(col_name)plt.show()

分箱操作

10 3000 5000 10000000

以5000為分割點 分割出高收入 低收入 進行映射 （減少數(shù)據(jù)之間的差異）

# 0 0 1 1 X[0] = pd.cut(X[0],bins = 5,labels = [0,1,2,3,4]) # 將數(shù)據(jù)進行切割，防止過擬合 X[0] 0 2 1 2 2 1 3 1 4 2.. 205 0 206 0 207 1 208 0 209 0 Name: 0, Length: 210, dtype: category Categories (5, int64): [0 < 1 < 2 < 3 < 4] sns.countplot(X[0]) C:\Anaconda\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.warnings.warn(<AxesSubplot:xlabel='0', ylabel='count'>

# 拆所有數(shù)據(jù) for col_name in X.columns:X[col_name] = pd.cut(X[col_name],bins = 5,labels = [0,1,2,3,4]) X 012345601234...205206207208209

2	2	2	2	2	0	1
2	2	3	1	2	0	1
1	1	4	1	2	1	0
1	1	3	1	2	0	0
2	2	4	2	3	0	1
...	...	...	...	...	...	...
0	0	3	0	1	1	0
0	0	1	0	0	2	1
1	1	3	0	2	4	1
0	0	1	0	0	1	1
0	0	2	0	1	3	1

210 rows × 7 columns

knn = KNeighborsClassifier() X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2,random_state = 1) knn.fit(X_train,y_train) KNeighborsClassifier() knn.score(X_train,y_train) 0.9166666666666666 knn.score(X_test,y_test) 0.9523809523809523

總結(jié)

以上是生活随笔為你收集整理的机器学习实战：小麦种子（封装函数进行调参、标准化、绘图查看数据分布）的全部內(nèi)容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網(wǎng)站內(nèi)容還不錯，歡迎將生活随笔推薦給好友。