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【Python機(jī)器學(xué)習(xí)】實(shí)驗(yàn)04(1) 多分類(lèi)(基于邏輯回歸)實(shí)踐

2年前作者：Want595分類(lèi)：Toy博客閱讀(18)違法舉報(bào)

這篇具有很好參考價(jià)值的文章主要介紹了【Python機(jī)器學(xué)習(xí)】實(shí)驗(yàn)04(1) 多分類(lèi)(基于邏輯回歸)實(shí)踐。希望對(duì)大家有所幫助。如果存在錯(cuò)誤或未考慮完全的地方，請(qǐng)大家不吝賜教，您也可以點(diǎn)擊"舉報(bào)違法"按鈕提交疑問(wèn)。

多分類(lèi)以及機(jī)器學(xué)習(xí)實(shí)踐

如何對(duì)多個(gè)類(lèi)別進(jìn)行分類(lèi)

Iris數(shù)據(jù)集是常用的分類(lèi)實(shí)驗(yàn)數(shù)據(jù)集，由Fisher, 1936收集整理。Iris也稱(chēng)鳶尾花卉數(shù)據(jù)集，是一類(lèi)多重變量分析的數(shù)據(jù)集。數(shù)據(jù)集包含150個(gè)數(shù)據(jù)樣本，分為3類(lèi)，每類(lèi)50個(gè)數(shù)據(jù)，每個(gè)數(shù)據(jù)包含4個(gè)屬性?？赏ㄟ^(guò)花萼長(zhǎng)度，花萼寬度，花瓣長(zhǎng)度，花瓣寬度4個(gè)屬性預(yù)測(cè)鳶尾花卉屬于（Setosa，Versicolour，Virginica）三個(gè)種類(lèi)中的哪一類(lèi)。

iris以鳶尾花的特征作為數(shù)據(jù)來(lái)源，常用在分類(lèi)操作中。該數(shù)據(jù)集由3種不同類(lèi)型的鳶尾花的各50個(gè)樣本數(shù)據(jù)構(gòu)成。其中的一個(gè)種類(lèi)與另外兩個(gè)種類(lèi)是線(xiàn)性可分離的，后兩個(gè)種類(lèi)是非線(xiàn)性可分離的。

該數(shù)據(jù)集包含了4個(gè)屬性：
Sepal.Length（花萼長(zhǎng)度），單位是cm;
Sepal.Width（花萼寬度），單位是cm;
Petal.Length（花瓣長(zhǎng)度），單位是cm;
Petal.Width（花瓣寬度），單位是cm;

種類(lèi)：Iris Setosa（山鳶尾）、Iris Versicolour（雜色鳶尾），以及Iris Virginica（維吉尼亞鳶尾）。

1.1 數(shù)據(jù)的預(yù)處理

import sklearn.datasets as datasets
import pandas as pd
import numpy as np

data=datasets.load_iris()
data

{'data': array([[5.1, 3.5, 1.4, 0.2],
        [4.9, 3. , 1.4, 0.2],
        [4.7, 3.2, 1.3, 0.2],
        [4.6, 3.1, 1.5, 0.2],
        [5. , 3.6, 1.4, 0.2],
        [5.4, 3.9, 1.7, 0.4],
        [4.6, 3.4, 1.4, 0.3],
        [5. , 3.4, 1.5, 0.2],
        [4.4, 2.9, 1.4, 0.2],
        [4.9, 3.1, 1.5, 0.1],
        [5.4, 3.7, 1.5, 0.2],
        [4.8, 3.4, 1.6, 0.2],
        [4.8, 3. , 1.4, 0.1],
        [4.3, 3. , 1.1, 0.1],
        [5.8, 4. , 1.2, 0.2],
        [5.7, 4.4, 1.5, 0.4],
        [5.4, 3.9, 1.3, 0.4],
        [5.1, 3.5, 1.4, 0.3],
        [5.7, 3.8, 1.7, 0.3],
        [5.1, 3.8, 1.5, 0.3],
        [5.4, 3.4, 1.7, 0.2],
        [5.1, 3.7, 1.5, 0.4],
        [4.6, 3.6, 1. , 0.2],
        [5.1, 3.3, 1.7, 0.5],
        [4.8, 3.4, 1.9, 0.2],
        [5. , 3. , 1.6, 0.2],
        [5. , 3.4, 1.6, 0.4],
        [5.2, 3.5, 1.5, 0.2],
        [5.2, 3.4, 1.4, 0.2],
        [4.7, 3.2, 1.6, 0.2],
        [4.8, 3.1, 1.6, 0.2],
        [5.4, 3.4, 1.5, 0.4],
        [5.2, 4.1, 1.5, 0.1],
        [5.5, 4.2, 1.4, 0.2],
        [4.9, 3.1, 1.5, 0.2],
        [5. , 3.2, 1.2, 0.2],
        [5.5, 3.5, 1.3, 0.2],
        [4.9, 3.6, 1.4, 0.1],
        [4.4, 3. , 1.3, 0.2],
        [5.1, 3.4, 1.5, 0.2],
        [5. , 3.5, 1.3, 0.3],
        [4.5, 2.3, 1.3, 0.3],
        [4.4, 3.2, 1.3, 0.2],
        [5. , 3.5, 1.6, 0.6],
        [5.1, 3.8, 1.9, 0.4],
        [4.8, 3. , 1.4, 0.3],
        [5.1, 3.8, 1.6, 0.2],
        [4.6, 3.2, 1.4, 0.2],
        [5.3, 3.7, 1.5, 0.2],
        [5. , 3.3, 1.4, 0.2],
        [7. , 3.2, 4.7, 1.4],
        [6.4, 3.2, 4.5, 1.5],
        [6.9, 3.1, 4.9, 1.5],
        [5.5, 2.3, 4. , 1.3],
        [6.5, 2.8, 4.6, 1.5],
        [5.7, 2.8, 4.5, 1.3],
        [6.3, 3.3, 4.7, 1.6],
        [4.9, 2.4, 3.3, 1. ],
        [6.6, 2.9, 4.6, 1.3],
        [5.2, 2.7, 3.9, 1.4],
        [5. , 2. , 3.5, 1. ],
        [5.9, 3. , 4.2, 1.5],
        [6. , 2.2, 4. , 1. ],
        [6.1, 2.9, 4.7, 1.4],
        [5.6, 2.9, 3.6, 1.3],
        [6.7, 3.1, 4.4, 1.4],
        [5.6, 3. , 4.5, 1.5],
        [5.8, 2.7, 4.1, 1. ],
        [6.2, 2.2, 4.5, 1.5],
        [5.6, 2.5, 3.9, 1.1],
        [5.9, 3.2, 4.8, 1.8],
        [6.1, 2.8, 4. , 1.3],
        [6.3, 2.5, 4.9, 1.5],
        [6.1, 2.8, 4.7, 1.2],
        [6.4, 2.9, 4.3, 1.3],
        [6.6, 3. , 4.4, 1.4],
        [6.8, 2.8, 4.8, 1.4],
        [6.7, 3. , 5. , 1.7],
        [6. , 2.9, 4.5, 1.5],
        [5.7, 2.6, 3.5, 1. ],
        [5.5, 2.4, 3.8, 1.1],
        [5.5, 2.4, 3.7, 1. ],
        [5.8, 2.7, 3.9, 1.2],
        [6. , 2.7, 5.1, 1.6],
        [5.4, 3. , 4.5, 1.5],
        [6. , 3.4, 4.5, 1.6],
        [6.7, 3.1, 4.7, 1.5],
        [6.3, 2.3, 4.4, 1.3],
        [5.6, 3. , 4.1, 1.3],
        [5.5, 2.5, 4. , 1.3],
        [5.5, 2.6, 4.4, 1.2],
        [6.1, 3. , 4.6, 1.4],
        [5.8, 2.6, 4. , 1.2],
        [5. , 2.3, 3.3, 1. ],
        [5.6, 2.7, 4.2, 1.3],
        [5.7, 3. , 4.2, 1.2],
        [5.7, 2.9, 4.2, 1.3],
        [6.2, 2.9, 4.3, 1.3],
        [5.1, 2.5, 3. , 1.1],
        [5.7, 2.8, 4.1, 1.3],
        [6.3, 3.3, 6. , 2.5],
        [5.8, 2.7, 5.1, 1.9],
        [7.1, 3. , 5.9, 2.1],
        [6.3, 2.9, 5.6, 1.8],
        [6.5, 3. , 5.8, 2.2],
        [7.6, 3. , 6.6, 2.1],
        [4.9, 2.5, 4.5, 1.7],
        [7.3, 2.9, 6.3, 1.8],
        [6.7, 2.5, 5.8, 1.8],
        [7.2, 3.6, 6.1, 2.5],
        [6.5, 3.2, 5.1, 2. ],
        [6.4, 2.7, 5.3, 1.9],
        [6.8, 3. , 5.5, 2.1],
        [5.7, 2.5, 5. , 2. ],
        [5.8, 2.8, 5.1, 2.4],
        [6.4, 3.2, 5.3, 2.3],
        [6.5, 3. , 5.5, 1.8],
        [7.7, 3.8, 6.7, 2.2],
        [7.7, 2.6, 6.9, 2.3],
        [6. , 2.2, 5. , 1.5],
        [6.9, 3.2, 5.7, 2.3],
        [5.6, 2.8, 4.9, 2. ],
        [7.7, 2.8, 6.7, 2. ],
        [6.3, 2.7, 4.9, 1.8],
        [6.7, 3.3, 5.7, 2.1],
        [7.2, 3.2, 6. , 1.8],
        [6.2, 2.8, 4.8, 1.8],
        [6.1, 3. , 4.9, 1.8],
        [6.4, 2.8, 5.6, 2.1],
        [7.2, 3. , 5.8, 1.6],
        [7.4, 2.8, 6.1, 1.9],
        [7.9, 3.8, 6.4, 2. ],
        [6.4, 2.8, 5.6, 2.2],
        [6.3, 2.8, 5.1, 1.5],
        [6.1, 2.6, 5.6, 1.4],
        [7.7, 3. , 6.1, 2.3],
        [6.3, 3.4, 5.6, 2.4],
        [6.4, 3.1, 5.5, 1.8],
        [6. , 3. , 4.8, 1.8],
        [6.9, 3.1, 5.4, 2.1],
        [6.7, 3.1, 5.6, 2.4],
        [6.9, 3.1, 5.1, 2.3],
        [5.8, 2.7, 5.1, 1.9],
        [6.8, 3.2, 5.9, 2.3],
        [6.7, 3.3, 5.7, 2.5],
        [6.7, 3. , 5.2, 2.3],
        [6.3, 2.5, 5. , 1.9],
        [6.5, 3. , 5.2, 2. ],
        [6.2, 3.4, 5.4, 2.3],
        [5.9, 3. , 5.1, 1.8]]),
 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]),
 'frame': None,
 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'),
 'DESCR': '.. _iris_dataset:\n\nIris plants dataset\n--------------------\n\n**Data Set Characteristics:**\n\n    :Number of Instances: 150 (50 in each of three classes)\n    :Number of Attributes: 4 numeric, predictive attributes and the class\n    :Attribute Information:\n        - sepal length in cm\n        - sepal width in cm\n        - petal length in cm\n        - petal width in cm\n        - class:\n                - Iris-Setosa\n                - Iris-Versicolour\n                - Iris-Virginica\n                \n    :Summary Statistics:\n\n    ============== ==== ==== ======= ===== ====================\n                    Min  Max   Mean    SD   Class Correlation\n    ============== ==== ==== ======= ===== ====================\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n    ============== ==== ==== ======= ===== ====================\n\n    :Missing Attribute Values: None\n    :Class Distribution: 33.3% for each of 3 classes.\n    :Creator: R.A. Fisher\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n    :Date: July, 1988\n\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\nfrom Fisher\'s paper. Note that it\'s the same as in R, but not as in the UCI\nMachine Learning Repository, which has two wrong data points.\n\nThis is perhaps the best known database to be found in the\npattern recognition literature.  Fisher\'s paper is a classic in the field and\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\ndata set contains 3 classes of 50 instances each, where each class refers to a\ntype of iris plant.  One class is linearly separable from the other 2; the\nlatter are NOT linearly separable from each other.\n\n.. topic:: References\n\n   - Fisher, R.A. "The use of multiple measurements in taxonomic problems"\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to\n     Mathematical Statistics" (John Wiley, NY, 1950).\n   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n   - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System\n     Structure and Classification Rule for Recognition in Partially Exposed\n     Environments".  IEEE Transactions on Pattern Analysis and Machine\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n   - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE Transactions\n     on Information Theory, May 1972, 431-433.\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al"s AUTOCLASS II\n     conceptual clustering system finds 3 classes in the data.\n   - Many, many more ...',
 'feature_names': ['sepal length (cm)',
  'sepal width (cm)',
  'petal length (cm)',
  'petal width (cm)'],
 'filename': 'iris.csv',
 'data_module': 'sklearn.datasets.data'}

data_x=data["data"]
data_y=data["target"]

data_x.shape,data_y.shape

((150, 4), (150,))

data_y=data_y.reshape([len(data_y),1])
data_y

array([[0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2]])

#法1 ，用拼接的方法
data=np.hstack([data_x,data_y])

#法二： 用插入的方法
np.insert(data_x,data_x.shape[1],data_y,axis=1)

array([[5.1, 3.5, 1.4, ..., 2. , 2. , 2. ],
       [4.9, 3. , 1.4, ..., 2. , 2. , 2. ],
       [4.7, 3.2, 1.3, ..., 2. , 2. , 2. ],
       ...,
       [6.5, 3. , 5.2, ..., 2. , 2. , 2. ],
       [6.2, 3.4, 5.4, ..., 2. , 2. , 2. ],
       [5.9, 3. , 5.1, ..., 2. , 2. , 2. ]])

data=pd.DataFrame(data,columns=["F1","F2","F3","F4","target"])
data

	F1	F2	F3	F4	target
0	5.1	3.5	1.4	0.2	0.0
1	4.9	3.0	1.4	0.2	0.0
2	4.7	3.2	1.3	0.2	0.0
3	4.6	3.1	1.5	0.2	0.0
4	5.0	3.6	1.4	0.2	0.0
...	...	...	...	...	...
145	6.7	3.0	5.2	2.3	2.0
146	6.3	2.5	5.0	1.9	2.0
147	6.5	3.0	5.2	2.0	2.0
148	6.2	3.4	5.4	2.3	2.0
149	5.9	3.0	5.1	1.8	2.0

150 rows × 5 columns

data.insert(0,"ones",1)

data

	ones	F1	F2	F3	F4	target
0	1	5.1	3.5	1.4	0.2	0.0
1	1	4.9	3.0	1.4	0.2	0.0
2	1	4.7	3.2	1.3	0.2	0.0
3	1	4.6	3.1	1.5	0.2	0.0
4	1	5.0	3.6	1.4	0.2	0.0
...	...	...	...	...	...	...
145	1	6.7	3.0	5.2	2.3	2.0
146	1	6.3	2.5	5.0	1.9	2.0
147	1	6.5	3.0	5.2	2.0	2.0
148	1	6.2	3.4	5.4	2.3	2.0
149	1	5.9	3.0	5.1	1.8	2.0

150 rows × 6 columns

data["target"]=data["target"].astype("int32")

data

	ones	F1	F2	F3	F4	target
0	1	5.1	3.5	1.4	0.2	0
1	1	4.9	3.0	1.4	0.2	0
2	1	4.7	3.2	1.3	0.2	0
3	1	4.6	3.1	1.5	0.2	0
4	1	5.0	3.6	1.4	0.2	0
...	...	...	...	...	...	...
145	1	6.7	3.0	5.2	2.3	2
146	1	6.3	2.5	5.0	1.9	2
147	1	6.5	3.0	5.2	2.0	2
148	1	6.2	3.4	5.4	2.3	2
149	1	5.9	3.0	5.1	1.8	2

150 rows × 6 columns

1.2 訓(xùn)練數(shù)據(jù)的準(zhǔn)備

data_x

array([[5.1, 3.5, 1.4, 0.2],
       [4.9, 3. , 1.4, 0.2],
       [4.7, 3.2, 1.3, 0.2],
       [4.6, 3.1, 1.5, 0.2],
       [5. , 3.6, 1.4, 0.2],
       [5.4, 3.9, 1.7, 0.4],
       [4.6, 3.4, 1.4, 0.3],
       [5. , 3.4, 1.5, 0.2],
       [4.4, 2.9, 1.4, 0.2],
       [4.9, 3.1, 1.5, 0.1],
       [5.4, 3.7, 1.5, 0.2],
       [4.8, 3.4, 1.6, 0.2],
       [4.8, 3. , 1.4, 0.1],
       [4.3, 3. , 1.1, 0.1],
       [5.8, 4. , 1.2, 0.2],
       [5.7, 4.4, 1.5, 0.4],
       [5.4, 3.9, 1.3, 0.4],
       [5.1, 3.5, 1.4, 0.3],
       [5.7, 3.8, 1.7, 0.3],
       [5.1, 3.8, 1.5, 0.3],
       [5.4, 3.4, 1.7, 0.2],
       [5.1, 3.7, 1.5, 0.4],
       [4.6, 3.6, 1. , 0.2],
       [5.1, 3.3, 1.7, 0.5],
       [4.8, 3.4, 1.9, 0.2],
       [5. , 3. , 1.6, 0.2],
       [5. , 3.4, 1.6, 0.4],
       [5.2, 3.5, 1.5, 0.2],
       [5.2, 3.4, 1.4, 0.2],
       [4.7, 3.2, 1.6, 0.2],
       [4.8, 3.1, 1.6, 0.2],
       [5.4, 3.4, 1.5, 0.4],
       [5.2, 4.1, 1.5, 0.1],
       [5.5, 4.2, 1.4, 0.2],
       [4.9, 3.1, 1.5, 0.2],
       [5. , 3.2, 1.2, 0.2],
       [5.5, 3.5, 1.3, 0.2],
       [4.9, 3.6, 1.4, 0.1],
       [4.4, 3. , 1.3, 0.2],
       [5.1, 3.4, 1.5, 0.2],
       [5. , 3.5, 1.3, 0.3],
       [4.5, 2.3, 1.3, 0.3],
       [4.4, 3.2, 1.3, 0.2],
       [5. , 3.5, 1.6, 0.6],
       [5.1, 3.8, 1.9, 0.4],
       [4.8, 3. , 1.4, 0.3],
       [5.1, 3.8, 1.6, 0.2],
       [4.6, 3.2, 1.4, 0.2],
       [5.3, 3.7, 1.5, 0.2],
       [5. , 3.3, 1.4, 0.2],
       [7. , 3.2, 4.7, 1.4],
       [6.4, 3.2, 4.5, 1.5],
       [6.9, 3.1, 4.9, 1.5],
       [5.5, 2.3, 4. , 1.3],
       [6.5, 2.8, 4.6, 1.5],
       [5.7, 2.8, 4.5, 1.3],
       [6.3, 3.3, 4.7, 1.6],
       [4.9, 2.4, 3.3, 1. ],
       [6.6, 2.9, 4.6, 1.3],
       [5.2, 2.7, 3.9, 1.4],
       [5. , 2. , 3.5, 1. ],
       [5.9, 3. , 4.2, 1.5],
       [6. , 2.2, 4. , 1. ],
       [6.1, 2.9, 4.7, 1.4],
       [5.6, 2.9, 3.6, 1.3],
       [6.7, 3.1, 4.4, 1.4],
       [5.6, 3. , 4.5, 1.5],
       [5.8, 2.7, 4.1, 1. ],
       [6.2, 2.2, 4.5, 1.5],
       [5.6, 2.5, 3.9, 1.1],
       [5.9, 3.2, 4.8, 1.8],
       [6.1, 2.8, 4. , 1.3],
       [6.3, 2.5, 4.9, 1.5],
       [6.1, 2.8, 4.7, 1.2],
       [6.4, 2.9, 4.3, 1.3],
       [6.6, 3. , 4.4, 1.4],
       [6.8, 2.8, 4.8, 1.4],
       [6.7, 3. , 5. , 1.7],
       [6. , 2.9, 4.5, 1.5],
       [5.7, 2.6, 3.5, 1. ],
       [5.5, 2.4, 3.8, 1.1],
       [5.5, 2.4, 3.7, 1. ],
       [5.8, 2.7, 3.9, 1.2],
       [6. , 2.7, 5.1, 1.6],
       [5.4, 3. , 4.5, 1.5],
       [6. , 3.4, 4.5, 1.6],
       [6.7, 3.1, 4.7, 1.5],
       [6.3, 2.3, 4.4, 1.3],
       [5.6, 3. , 4.1, 1.3],
       [5.5, 2.5, 4. , 1.3],
       [5.5, 2.6, 4.4, 1.2],
       [6.1, 3. , 4.6, 1.4],
       [5.8, 2.6, 4. , 1.2],
       [5. , 2.3, 3.3, 1. ],
       [5.6, 2.7, 4.2, 1.3],
       [5.7, 3. , 4.2, 1.2],
       [5.7, 2.9, 4.2, 1.3],
       [6.2, 2.9, 4.3, 1.3],
       [5.1, 2.5, 3. , 1.1],
       [5.7, 2.8, 4.1, 1.3],
       [6.3, 3.3, 6. , 2.5],
       [5.8, 2.7, 5.1, 1.9],
       [7.1, 3. , 5.9, 2.1],
       [6.3, 2.9, 5.6, 1.8],
       [6.5, 3. , 5.8, 2.2],
       [7.6, 3. , 6.6, 2.1],
       [4.9, 2.5, 4.5, 1.7],
       [7.3, 2.9, 6.3, 1.8],
       [6.7, 2.5, 5.8, 1.8],
       [7.2, 3.6, 6.1, 2.5],
       [6.5, 3.2, 5.1, 2. ],
       [6.4, 2.7, 5.3, 1.9],
       [6.8, 3. , 5.5, 2.1],
       [5.7, 2.5, 5. , 2. ],
       [5.8, 2.8, 5.1, 2.4],
       [6.4, 3.2, 5.3, 2.3],
       [6.5, 3. , 5.5, 1.8],
       [7.7, 3.8, 6.7, 2.2],
       [7.7, 2.6, 6.9, 2.3],
       [6. , 2.2, 5. , 1.5],
       [6.9, 3.2, 5.7, 2.3],
       [5.6, 2.8, 4.9, 2. ],
       [7.7, 2.8, 6.7, 2. ],
       [6.3, 2.7, 4.9, 1.8],
       [6.7, 3.3, 5.7, 2.1],
       [7.2, 3.2, 6. , 1.8],
       [6.2, 2.8, 4.8, 1.8],
       [6.1, 3. , 4.9, 1.8],
       [6.4, 2.8, 5.6, 2.1],
       [7.2, 3. , 5.8, 1.6],
       [7.4, 2.8, 6.1, 1.9],
       [7.9, 3.8, 6.4, 2. ],
       [6.4, 2.8, 5.6, 2.2],
       [6.3, 2.8, 5.1, 1.5],
       [6.1, 2.6, 5.6, 1.4],
       [7.7, 3. , 6.1, 2.3],
       [6.3, 3.4, 5.6, 2.4],
       [6.4, 3.1, 5.5, 1.8],
       [6. , 3. , 4.8, 1.8],
       [6.9, 3.1, 5.4, 2.1],
       [6.7, 3.1, 5.6, 2.4],
       [6.9, 3.1, 5.1, 2.3],
       [5.8, 2.7, 5.1, 1.9],
       [6.8, 3.2, 5.9, 2.3],
       [6.7, 3.3, 5.7, 2.5],
       [6.7, 3. , 5.2, 2.3],
       [6.3, 2.5, 5. , 1.9],
       [6.5, 3. , 5.2, 2. ],
       [6.2, 3.4, 5.4, 2.3],
       [5.9, 3. , 5.1, 1.8]])

data_x=np.insert(data_x,0,1,axis=1)

data_x.shape,data_y.shape

((150, 5), (150, 1))

#訓(xùn)練數(shù)據(jù)的特征和標(biāo)簽
data_x,data_y

(array([[1. , 5.1, 3.5, 1.4, 0.2],
        [1. , 4.9, 3. , 1.4, 0.2],
        [1. , 4.7, 3.2, 1.3, 0.2],
        [1. , 4.6, 3.1, 1.5, 0.2],
        [1. , 5. , 3.6, 1.4, 0.2],
        [1. , 5.4, 3.9, 1.7, 0.4],
        [1. , 4.6, 3.4, 1.4, 0.3],
        [1. , 5. , 3.4, 1.5, 0.2],
        [1. , 4.4, 2.9, 1.4, 0.2],
        [1. , 4.9, 3.1, 1.5, 0.1],
        [1. , 5.4, 3.7, 1.5, 0.2],
        [1. , 4.8, 3.4, 1.6, 0.2],
        [1. , 4.8, 3. , 1.4, 0.1],
        [1. , 4.3, 3. , 1.1, 0.1],
        [1. , 5.8, 4. , 1.2, 0.2],
        [1. , 5.7, 4.4, 1.5, 0.4],
        [1. , 5.4, 3.9, 1.3, 0.4],
        [1. , 5.1, 3.5, 1.4, 0.3],
        [1. , 5.7, 3.8, 1.7, 0.3],
        [1. , 5.1, 3.8, 1.5, 0.3],
        [1. , 5.4, 3.4, 1.7, 0.2],
        [1. , 5.1, 3.7, 1.5, 0.4],
        [1. , 4.6, 3.6, 1. , 0.2],
        [1. , 5.1, 3.3, 1.7, 0.5],
        [1. , 4.8, 3.4, 1.9, 0.2],
        [1. , 5. , 3. , 1.6, 0.2],
        [1. , 5. , 3.4, 1.6, 0.4],
        [1. , 5.2, 3.5, 1.5, 0.2],
        [1. , 5.2, 3.4, 1.4, 0.2],
        [1. , 4.7, 3.2, 1.6, 0.2],
        [1. , 4.8, 3.1, 1.6, 0.2],
        [1. , 5.4, 3.4, 1.5, 0.4],
        [1. , 5.2, 4.1, 1.5, 0.1],
        [1. , 5.5, 4.2, 1.4, 0.2],
        [1. , 4.9, 3.1, 1.5, 0.2],
        [1. , 5. , 3.2, 1.2, 0.2],
        [1. , 5.5, 3.5, 1.3, 0.2],
        [1. , 4.9, 3.6, 1.4, 0.1],
        [1. , 4.4, 3. , 1.3, 0.2],
        [1. , 5.1, 3.4, 1.5, 0.2],
        [1. , 5. , 3.5, 1.3, 0.3],
        [1. , 4.5, 2.3, 1.3, 0.3],
        [1. , 4.4, 3.2, 1.3, 0.2],
        [1. , 5. , 3.5, 1.6, 0.6],
        [1. , 5.1, 3.8, 1.9, 0.4],
        [1. , 4.8, 3. , 1.4, 0.3],
        [1. , 5.1, 3.8, 1.6, 0.2],
        [1. , 4.6, 3.2, 1.4, 0.2],
        [1. , 5.3, 3.7, 1.5, 0.2],
        [1. , 5. , 3.3, 1.4, 0.2],
        [1. , 7. , 3.2, 4.7, 1.4],
        [1. , 6.4, 3.2, 4.5, 1.5],
        [1. , 6.9, 3.1, 4.9, 1.5],
        [1. , 5.5, 2.3, 4. , 1.3],
        [1. , 6.5, 2.8, 4.6, 1.5],
        [1. , 5.7, 2.8, 4.5, 1.3],
        [1. , 6.3, 3.3, 4.7, 1.6],
        [1. , 4.9, 2.4, 3.3, 1. ],
        [1. , 6.6, 2.9, 4.6, 1.3],
        [1. , 5.2, 2.7, 3.9, 1.4],
        [1. , 5. , 2. , 3.5, 1. ],
        [1. , 5.9, 3. , 4.2, 1.5],
        [1. , 6. , 2.2, 4. , 1. ],
        [1. , 6.1, 2.9, 4.7, 1.4],
        [1. , 5.6, 2.9, 3.6, 1.3],
        [1. , 6.7, 3.1, 4.4, 1.4],
        [1. , 5.6, 3. , 4.5, 1.5],
        [1. , 5.8, 2.7, 4.1, 1. ],
        [1. , 6.2, 2.2, 4.5, 1.5],
        [1. , 5.6, 2.5, 3.9, 1.1],
        [1. , 5.9, 3.2, 4.8, 1.8],
        [1. , 6.1, 2.8, 4. , 1.3],
        [1. , 6.3, 2.5, 4.9, 1.5],
        [1. , 6.1, 2.8, 4.7, 1.2],
        [1. , 6.4, 2.9, 4.3, 1.3],
        [1. , 6.6, 3. , 4.4, 1.4],
        [1. , 6.8, 2.8, 4.8, 1.4],
        [1. , 6.7, 3. , 5. , 1.7],
        [1. , 6. , 2.9, 4.5, 1.5],
        [1. , 5.7, 2.6, 3.5, 1. ],
        [1. , 5.5, 2.4, 3.8, 1.1],
        [1. , 5.5, 2.4, 3.7, 1. ],
        [1. , 5.8, 2.7, 3.9, 1.2],
        [1. , 6. , 2.7, 5.1, 1.6],
        [1. , 5.4, 3. , 4.5, 1.5],
        [1. , 6. , 3.4, 4.5, 1.6],
        [1. , 6.7, 3.1, 4.7, 1.5],
        [1. , 6.3, 2.3, 4.4, 1.3],
        [1. , 5.6, 3. , 4.1, 1.3],
        [1. , 5.5, 2.5, 4. , 1.3],
        [1. , 5.5, 2.6, 4.4, 1.2],
        [1. , 6.1, 3. , 4.6, 1.4],
        [1. , 5.8, 2.6, 4. , 1.2],
        [1. , 5. , 2.3, 3.3, 1. ],
        [1. , 5.6, 2.7, 4.2, 1.3],
        [1. , 5.7, 3. , 4.2, 1.2],
        [1. , 5.7, 2.9, 4.2, 1.3],
        [1. , 6.2, 2.9, 4.3, 1.3],
        [1. , 5.1, 2.5, 3. , 1.1],
        [1. , 5.7, 2.8, 4.1, 1.3],
        [1. , 6.3, 3.3, 6. , 2.5],
        [1. , 5.8, 2.7, 5.1, 1.9],
        [1. , 7.1, 3. , 5.9, 2.1],
        [1. , 6.3, 2.9, 5.6, 1.8],
        [1. , 6.5, 3. , 5.8, 2.2],
        [1. , 7.6, 3. , 6.6, 2.1],
        [1. , 4.9, 2.5, 4.5, 1.7],
        [1. , 7.3, 2.9, 6.3, 1.8],
        [1. , 6.7, 2.5, 5.8, 1.8],
        [1. , 7.2, 3.6, 6.1, 2.5],
        [1. , 6.5, 3.2, 5.1, 2. ],
        [1. , 6.4, 2.7, 5.3, 1.9],
        [1. , 6.8, 3. , 5.5, 2.1],
        [1. , 5.7, 2.5, 5. , 2. ],
        [1. , 5.8, 2.8, 5.1, 2.4],
        [1. , 6.4, 3.2, 5.3, 2.3],
        [1. , 6.5, 3. , 5.5, 1.8],
        [1. , 7.7, 3.8, 6.7, 2.2],
        [1. , 7.7, 2.6, 6.9, 2.3],
        [1. , 6. , 2.2, 5. , 1.5],
        [1. , 6.9, 3.2, 5.7, 2.3],
        [1. , 5.6, 2.8, 4.9, 2. ],
        [1. , 7.7, 2.8, 6.7, 2. ],
        [1. , 6.3, 2.7, 4.9, 1.8],
        [1. , 6.7, 3.3, 5.7, 2.1],
        [1. , 7.2, 3.2, 6. , 1.8],
        [1. , 6.2, 2.8, 4.8, 1.8],
        [1. , 6.1, 3. , 4.9, 1.8],
        [1. , 6.4, 2.8, 5.6, 2.1],
        [1. , 7.2, 3. , 5.8, 1.6],
        [1. , 7.4, 2.8, 6.1, 1.9],
        [1. , 7.9, 3.8, 6.4, 2. ],
        [1. , 6.4, 2.8, 5.6, 2.2],
        [1. , 6.3, 2.8, 5.1, 1.5],
        [1. , 6.1, 2.6, 5.6, 1.4],
        [1. , 7.7, 3. , 6.1, 2.3],
        [1. , 6.3, 3.4, 5.6, 2.4],
        [1. , 6.4, 3.1, 5.5, 1.8],
        [1. , 6. , 3. , 4.8, 1.8],
        [1. , 6.9, 3.1, 5.4, 2.1],
        [1. , 6.7, 3.1, 5.6, 2.4],
        [1. , 6.9, 3.1, 5.1, 2.3],
        [1. , 5.8, 2.7, 5.1, 1.9],
        [1. , 6.8, 3.2, 5.9, 2.3],
        [1. , 6.7, 3.3, 5.7, 2.5],
        [1. , 6.7, 3. , 5.2, 2.3],
        [1. , 6.3, 2.5, 5. , 1.9],
        [1. , 6.5, 3. , 5.2, 2. ],
        [1. , 6.2, 3.4, 5.4, 2.3],
        [1. , 5.9, 3. , 5.1, 1.8]]),
 array([[0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [0],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [1],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2],
        [2]]))

由于有三個(gè)類(lèi)別，那么在訓(xùn)練時(shí)三類(lèi)數(shù)據(jù)要分開(kāi)

data1=data.copy()

data1

	ones	F1	F2	F3	F4	target
0	1	5.1	3.5	1.4	0.2	0
1	1	4.9	3.0	1.4	0.2	0
2	1	4.7	3.2	1.3	0.2	0
3	1	4.6	3.1	1.5	0.2	0
4	1	5.0	3.6	1.4	0.2	0
...	...	...	...	...	...	...
145	1	6.7	3.0	5.2	2.3	2
146	1	6.3	2.5	5.0	1.9	2
147	1	6.5	3.0	5.2	2.0	2
148	1	6.2	3.4	5.4	2.3	2
149	1	5.9	3.0	5.1	1.8	2

150 rows × 6 columns

data

data1.loc[data["target"]!=0,"target"]=0
data1.loc[data["target"]==0,"target"]=1

data1

	ones	F1	F2	F3	F4	target
0	1	5.1	3.5	1.4	0.2	1
1	1	4.9	3.0	1.4	0.2	1
2	1	4.7	3.2	1.3	0.2	1
3	1	4.6	3.1	1.5	0.2	1
4	1	5.0	3.6	1.4	0.2	1
...	...	...	...	...	...	...
145	1	6.7	3.0	5.2	2.3	0
146	1	6.3	2.5	5.0	1.9	0
147	1	6.5	3.0	5.2	2.0	0
148	1	6.2	3.4	5.4	2.3	0
149	1	5.9	3.0	5.1	1.8	0

150 rows × 6 columns

data1_x=data1.iloc[:,:data1.shape[1]-1].values
data1_y=data1.iloc[:,data1.shape[1]-1].values
data1_x.shape,data1_y.shape

((150, 5), (150,))

#針對(duì)第二類(lèi)，即第二個(gè)分類(lèi)器的數(shù)據(jù)
data2=data.copy()
data2.loc[data["target"]==1,"target"]=1
data2.loc[data["target"]!=1,"target"]=0
data2["target"]==0

0      True
1      True
2      True
3      True
4      True
       ... 
145    True
146    True
147    True
148    True
149    True
Name: target, Length: 150, dtype: bool

data2.shape[1]

data2.iloc[50:55,:]

	ones	F1	F2	F3	F4	target
50	1	7.0	3.2	4.7	1.4	1
51	1	6.4	3.2	4.5	1.5	1
52	1	6.9	3.1	4.9	1.5	1
53	1	5.5	2.3	4.0	1.3	1
54	1	6.5	2.8	4.6	1.5	1

data2_x=data2.iloc[:,:data2.shape[1]-1].values
data2_y=data2.iloc[:,data2.shape[1]-1].values

#針對(duì)第三類(lèi)，即第三個(gè)分類(lèi)器的數(shù)據(jù)
data3=data.copy()
data3.loc[data["target"]==2,"target"]=1
data3.loc[data["target"]!=2,"target"]=0
data3

	ones	F1	F2	F3	F4	target
0	1	5.1	3.5	1.4	0.2	0
1	1	4.9	3.0	1.4	0.2	0
2	1	4.7	3.2	1.3	0.2	0
3	1	4.6	3.1	1.5	0.2	0
4	1	5.0	3.6	1.4	0.2	0
...	...	...	...	...	...	...
145	1	6.7	3.0	5.2	2.3	1
146	1	6.3	2.5	5.0	1.9	1
147	1	6.5	3.0	5.2	2.0	1
148	1	6.2	3.4	5.4	2.3	1
149	1	5.9	3.0	5.1	1.8	1

150 rows × 6 columns

data3_x=data3.iloc[:,:data3.shape[1]-1].values
data3_y=data3.iloc[:,data3.shape[1]-1].values

1.3 定義假設(shè)函數(shù)，代價(jià)函數(shù)，梯度下降算法（從實(shí)驗(yàn)3復(fù)制過(guò)來(lái)）

def sigmoid(z):
    return 1 / (1 + np.exp(-z))

def h(X,w):
    z=X@w
    h=sigmoid(z)
    return h

#代價(jià)函數(shù)構(gòu)造
def cost(X,w,y):
    #當(dāng)X(m,n+1),y(m,),w(n+1,1)
    y_hat=sigmoid(X@w)
    right=np.multiply(y.ravel(),np.log(y_hat).ravel())+np.multiply((1-y).ravel(),np.log(1-y_hat).ravel())
    cost=-np.sum(right)/X.shape[0]
    return cost

def sigmoid(z):
    return 1 / (1 + np.exp(-z))

def h(X,w):
    z=X@w
    h=sigmoid(z)
    return h

#代價(jià)函數(shù)構(gòu)造
def cost(X,w,y):
    #當(dāng)X(m,n+1),y(m,),w(n+1,1)
    y_hat=sigmoid(X@w)
    right=np.multiply(y.ravel(),np.log(y_hat).ravel())+np.multiply((1-y).ravel(),np.log(1-y_hat).ravel())
    cost=-np.sum(right)/X.shape[0]
    return cost



def grandient(X,y,iter_num,alpha):
    y=y.reshape((X.shape[0],1))
    w=np.zeros((X.shape[1],1))
    cost_lst=[]  
    for i in range(iter_num):
        y_pred=h(X,w)-y
        temp=np.zeros((X.shape[1],1))
        for j in range(X.shape[1]):
            right=np.multiply(y_pred.ravel(),X[:,j])
            
            gradient=1/(X.shape[0])*(np.sum(right))
            temp[j,0]=w[j,0]-alpha*gradient
        w=temp
        cost_lst.append(cost(X,w,y.ravel()))
    return w,cost_lst

1.4 調(diào)用梯度下降算法來(lái)學(xué)習(xí)三個(gè)分類(lèi)模型的參數(shù)

#初始化超參數(shù)
iter_num,alpha=600000,0.001

#訓(xùn)練第一個(gè)模型
w1,cost_lst1=grandient(data1_x,data1_y,iter_num,alpha)

import matplotlib.pyplot as plt
plt.plot(range(iter_num),cost_lst1,"b-o")

[<matplotlib.lines.Line2D at 0x2562630b100>]

python多分類(lèi)邏輯回歸,《 Python機(jī)器學(xué)習(xí)入門(mén)實(shí)驗(yàn) 》,機(jī)器學(xué)習(xí),python,分類(lèi)

#訓(xùn)練第二個(gè)模型
w2,cost_lst2=grandient(data2_x,data2_y,iter_num,alpha)

import matplotlib.pyplot as plt
plt.plot(range(iter_num),cost_lst2,"b-o")

[<matplotlib.lines.Line2D at 0x25628114280>]

python多分類(lèi)邏輯回歸,《 Python機(jī)器學(xué)習(xí)入門(mén)實(shí)驗(yàn) 》,機(jī)器學(xué)習(xí),python,分類(lèi)

#訓(xùn)練第三個(gè)模型
w3,cost_lst3=grandient(data3_x,data3_y,iter_num,alpha)

w3

array([[-3.22437049],
       [-3.50214058],
       [-3.50286355],
       [ 5.16580317],
       [ 5.89898368]])

import matplotlib.pyplot as plt
plt.plot(range(iter_num),cost_lst3,"b-o")

[<matplotlib.lines.Line2D at 0x2562e0f81c0>]

python多分類(lèi)邏輯回歸,《 Python機(jī)器學(xué)習(xí)入門(mén)實(shí)驗(yàn) 》,機(jī)器學(xué)習(xí),python,分類(lèi)

1.5 利用模型進(jìn)行預(yù)測(cè)

h(data_x,w3)

array([[1.48445441e-11],
       [1.72343968e-10],
       [1.02798153e-10],
       [5.81975546e-10],
       [1.48434710e-11],
       [1.95971176e-11],
       [2.18959639e-10],
       [5.01346874e-11],
       [1.40930075e-09],
       [1.12830635e-10],
       [4.31888744e-12],
       [1.69308343e-10],
       [1.35613372e-10],
       [1.65858883e-10],
       [7.89880725e-14],
       [4.23224675e-13],
       [2.48199140e-12],
       [2.67766642e-11],
       [5.39314286e-12],
       [1.56935848e-11],
       [3.47096426e-11],
       [4.01827075e-11],
       [7.63005509e-12],
       [8.26864773e-10],
       [7.97484594e-10],
       [3.41189783e-10],
       [2.73442178e-10],
       [1.75314894e-11],
       [1.48456174e-11],
       [4.84204982e-10],
       [4.84239990e-10],
       [4.01914238e-11],
       [1.18813180e-12],
       [3.14985611e-13],
       [2.03524473e-10],
       [2.14461446e-11],
       [2.18189955e-12],
       [1.16799745e-11],
       [5.92281641e-10],
       [3.53217554e-11],
       [2.26727669e-11],
       [8.74004884e-09],
       [2.93949962e-10],
       [6.26783110e-10],
       [2.23513465e-10],
       [4.41246960e-10],
       [1.45841303e-11],
       [2.44584721e-10],
       [6.13010507e-12],
       [4.24539165e-11],
       [1.64123143e-03],
       [8.55503211e-03],
       [1.65105645e-02],
       [9.87814122e-02],
       [3.97290777e-02],
       [1.11076040e-01],
       [4.19003715e-02],
       [2.88426221e-03],
       [6.27161978e-03],
       [7.67020481e-02],
       [2.27204861e-02],
       [2.08212169e-02],
       [4.58067633e-03],
       [9.90450665e-02],
       [1.19419048e-03],
       [1.41462060e-03],
       [2.22638069e-01],
       [2.68940904e-03],
       [3.66014737e-01],
       [6.97791873e-03],
       [5.78803255e-01],
       [2.32071970e-03],
       [5.28941621e-01],
       [4.57649874e-02],
       [2.69208900e-03],
       [2.84603646e-03],
       [2.20421076e-02],
       [2.07507605e-01],
       [9.10460936e-02],
       [2.44824946e-04],
       [8.37509821e-03],
       [2.78543808e-03],
       [3.11283202e-03],
       [8.89831833e-01],
       [3.65880536e-01],
       [3.03993844e-02],
       [1.18930239e-02],
       [4.99150151e-02],
       [1.10252946e-02],
       [5.15923462e-02],
       [1.43653056e-01],
       [4.41610209e-02],
       [7.37513950e-03],
       [2.88447014e-03],
       [5.07366744e-02],
       [7.24617687e-03],
       [1.83460602e-02],
       [5.40874928e-03],
       [3.87210511e-04],
       [1.55791816e-02],
       [9.99862942e-01],
       [9.89637526e-01],
       [9.86183040e-01],
       [9.83705644e-01],
       [9.98410187e-01],
       [9.97834502e-01],
       [9.84208537e-01],
       [9.85434538e-01],
       [9.94141336e-01],
       [9.94561329e-01],
       [7.20333384e-01],
       [9.70431293e-01],
       [9.62754456e-01],
       [9.96609064e-01],
       [9.99222270e-01],
       [9.83684437e-01],
       [9.26437633e-01],
       [9.83486260e-01],
       [9.99950496e-01],
       [9.39002061e-01],
       [9.88043323e-01],
       [9.88637702e-01],
       [9.98357641e-01],
       [7.65848930e-01],
       [9.73006160e-01],
       [8.76969899e-01],
       [6.61137141e-01],
       [6.97324053e-01],
       [9.97185846e-01],
       [6.11033594e-01],
       [9.77494647e-01],
       [6.58573810e-01],
       [9.98437920e-01],
       [5.24529693e-01],
       [9.70465066e-01],
       [9.87624920e-01],
       [9.97236435e-01],
       [9.26432706e-01],
       [6.61104746e-01],
       [8.84442100e-01],
       [9.96082862e-01],
       [8.40940308e-01],
       [9.89637526e-01],
       [9.96974990e-01],
       [9.97386310e-01],
       [9.62040470e-01],
       [9.52214579e-01],
       [8.96902215e-01],
       [9.90200940e-01],
       [9.28785160e-01]])

#將數(shù)據(jù)輸入三個(gè)模型的看看結(jié)果
multi_pred=pd.DataFrame(zip(h(data_x,w1).ravel(),h(data_x,w2).ravel(),h(data_x,w3).ravel()))
multi_pred

	0	1	2
0	0.999297	0.108037	1.484454e-11
1	0.997061	0.270814	1.723440e-10
2	0.998633	0.164710	1.027982e-10
3	0.995774	0.231910	5.819755e-10
4	0.999415	0.085259	1.484347e-11
...	...	...	...
145	0.000007	0.127574	9.620405e-01
146	0.000006	0.496389	9.522146e-01
147	0.000010	0.234745	8.969022e-01
148	0.000006	0.058444	9.902009e-01
149	0.000014	0.284295	9.287852e-01

150 rows × 3 columns

multi_pred.values[:3]

array([[9.99297209e-01, 1.08037473e-01, 1.48445441e-11],
       [9.97060801e-01, 2.70813780e-01, 1.72343968e-10],
       [9.98632728e-01, 1.64709623e-01, 1.02798153e-10]])

#每個(gè)樣本的預(yù)測(cè)值
np.argmax(multi_pred.values,axis=1)

array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2,
       2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], dtype=int64)

#每個(gè)樣本的真實(shí)值
data_y

array([[0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [1],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2],
       [2]])

1.6 評(píng)估模型

np.argmax(multi_pred.values,axis=1)==data_y.ravel()

array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True, False,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True, False, False,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True, False,  True,  True,  True, False,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True])

np.sum(np.argmax(multi_pred.values,axis=1)==data_y.ravel())

np.sum(np.argmax(multi_pred.values,axis=1)==data_y.ravel())/len(data)

0.9666666666666667

1.7 試試sklearn

from sklearn.linear_model import LogisticRegression
#建立第一個(gè)模型
clf1=LogisticRegression()
clf1.fit(data1_x,data1_y)
#建立第二個(gè)模型
clf2=LogisticRegression()
clf2.fit(data2_x,data2_y)
#建立第三個(gè)模型
clf3=LogisticRegression()
clf3.fit(data3_x,data3_y)

LogisticRegression()

y_pred1=clf1.predict(data_x)
y_pred2=clf2.predict(data_x)
y_pred3=clf3.predict(data_x)

#可視化各模型的預(yù)測(cè)結(jié)果
multi_pred=pd.DataFrame(zip(y_pred1,y_pred2,y_pred3),columns=["模型1","模糊2","模型3"])
multi_pred

	模型1	模糊2	模型3
0	1	0	0
1	1	0	0
2	1	0	0
3	1	0	0
4	1	0	0
...	...	...	...
145	0	0	1
146	0	1	1
147	0	0	1
148	0	0	1
149	0	0	1

150 rows × 3 columns

#判斷預(yù)測(cè)結(jié)果
np.argmax(multi_pred.values,axis=1)

array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0,
       0, 1, 1, 1, 2, 0, 1, 1, 0, 0, 0, 2, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1,
       0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2,
       2, 2, 2, 1, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2,
       2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2], dtype=int64)

data_y.ravel()

array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])

#計(jì)算準(zhǔn)確率
np.sum(np.argmax(multi_pred.values,axis=1)==data_y.ravel())/data.shape[0]

0.7333333333333333

實(shí)驗(yàn)：請(qǐng)動(dòng)手完成你們第一個(gè)多分類(lèi)問(wèn)題，祝好運(yùn)！完成下面代碼

1. 數(shù)據(jù)讀取

data_x,data_y=datasets.make_blobs(n_samples=200, n_features=6,  centers=4,random_state=0)

data_x.shape,data_y.shape

((200, 6), (200,))

2. 訓(xùn)練數(shù)據(jù)的準(zhǔn)備

data=np.insert(data_x,data_x.shape[1],data_y,axis=1)

data=pd.DataFrame(data,columns=["F1","F2","F3","F4","F5","F6","target"])
data

	F1	F2	F3	F4	F5	F6	target
0	2.116632	7.972800	-9.328969	-8.224605	-12.178429	5.498447	2.0
1	1.886449	4.621006	2.841595	0.431245	-2.471350	2.507833	0.0
2	2.391329	6.464609	-9.805900	-7.289968	-9.650985	6.388460	2.0
3	-1.034776	6.626886	9.031235	-0.812908	5.449855	0.134062	1.0
4	-0.481593	8.191753	7.504717	-1.975688	6.649021	0.636824	1.0
...	...	...	...	...	...	...	...
195	5.434893	7.128471	9.789546	6.061382	0.634133	5.757024	3.0
196	-0.406625	7.586001	9.322750	-1.837333	6.477815	-0.992725	1.0
197	2.031462	7.804427	-8.539512	-9.824409	-10.046935	6.918085	2.0
198	4.081889	6.127685	11.091126	4.812011	-0.005915	5.342211	3.0
199	0.985744	7.285737	-8.395940	-6.586471	-9.651765	6.651012	2.0

200 rows × 7 columns

data["target"]=data["target"].astype("int32")

data

	F1	F2	F3	F4	F5	F6	target
0	2.116632	7.972800	-9.328969	-8.224605	-12.178429	5.498447	2
1	1.886449	4.621006	2.841595	0.431245	-2.471350	2.507833	0
2	2.391329	6.464609	-9.805900	-7.289968	-9.650985	6.388460	2
3	-1.034776	6.626886	9.031235	-0.812908	5.449855	0.134062	1
4	-0.481593	8.191753	7.504717	-1.975688	6.649021	0.636824	1
...	...	...	...	...	...	...	...
195	5.434893	7.128471	9.789546	6.061382	0.634133	5.757024	3
196	-0.406625	7.586001	9.322750	-1.837333	6.477815	-0.992725	1
197	2.031462	7.804427	-8.539512	-9.824409	-10.046935	6.918085	2
198	4.081889	6.127685	11.091126	4.812011	-0.005915	5.342211	3
199	0.985744	7.285737	-8.395940	-6.586471	-9.651765	6.651012	2

200 rows × 7 columns

data.insert(0,"ones",1)

data

	ones	F1	F2	F3	F4	F5	F6	target
0	1	2.116632	7.972800	-9.328969	-8.224605	-12.178429	5.498447	2
1	1	1.886449	4.621006	2.841595	0.431245	-2.471350	2.507833	0
2	1	2.391329	6.464609	-9.805900	-7.289968	-9.650985	6.388460	2
3	1	-1.034776	6.626886	9.031235	-0.812908	5.449855	0.134062	1
4	1	-0.481593	8.191753	7.504717	-1.975688	6.649021	0.636824	1
...	...	...	...	...	...	...	...	...
195	1	5.434893	7.128471	9.789546	6.061382	0.634133	5.757024	3
196	1	-0.406625	7.586001	9.322750	-1.837333	6.477815	-0.992725	1
197	1	2.031462	7.804427	-8.539512	-9.824409	-10.046935	6.918085	2
198	1	4.081889	6.127685	11.091126	4.812011	-0.005915	5.342211	3
199	1	0.985744	7.285737	-8.395940	-6.586471	-9.651765	6.651012	2

200 rows × 8 columns

#第一個(gè)類(lèi)別的數(shù)據(jù)
data1=data.copy()
data1.loc[data["target"]==0,"target"]=1
data1.loc[data["target"]!=0,"target"]=0
data1

	ones	F1	F2	F3	F4	F5	F6	target
0	1	2.116632	7.972800	-9.328969	-8.224605	-12.178429	5.498447	0
1	1	1.886449	4.621006	2.841595	0.431245	-2.471350	2.507833	1
2	1	2.391329	6.464609	-9.805900	-7.289968	-9.650985	6.388460	0
3	1	-1.034776	6.626886	9.031235	-0.812908	5.449855	0.134062	0
4	1	-0.481593	8.191753	7.504717	-1.975688	6.649021	0.636824	0
...	...	...	...	...	...	...	...	...
195	1	5.434893	7.128471	9.789546	6.061382	0.634133	5.757024	0
196	1	-0.406625	7.586001	9.322750	-1.837333	6.477815	-0.992725	0
197	1	2.031462	7.804427	-8.539512	-9.824409	-10.046935	6.918085	0
198	1	4.081889	6.127685	11.091126	4.812011	-0.005915	5.342211	0
199	1	0.985744	7.285737	-8.395940	-6.586471	-9.651765	6.651012	0

200 rows × 8 columns

data1_x=data1.iloc[:,:data1.shape[1]-1].values
data1_y=data1.iloc[:,data1.shape[1]-1].values
data1_x.shape,data1_y.shape

((200, 7), (200,))

#第二個(gè)類(lèi)別的數(shù)據(jù)
data2=data.copy()
data2.loc[data["target"]==1,"target"]=1
data2.loc[data["target"]!=1,"target"]=0
data2

	ones	F1	F2	F3	F4	F5	F6	target
0	1	2.116632	7.972800	-9.328969	-8.224605	-12.178429	5.498447	0
1	1	1.886449	4.621006	2.841595	0.431245	-2.471350	2.507833	0
2	1	2.391329	6.464609	-9.805900	-7.289968	-9.650985	6.388460	0
3	1	-1.034776	6.626886	9.031235	-0.812908	5.449855	0.134062	1
4	1	-0.481593	8.191753	7.504717	-1.975688	6.649021	0.636824	1
...	...	...	...	...	...	...	...	...
195	1	5.434893	7.128471	9.789546	6.061382	0.634133	5.757024	0
196	1	-0.406625	7.586001	9.322750	-1.837333	6.477815	-0.992725	1
197	1	2.031462	7.804427	-8.539512	-9.824409	-10.046935	6.918085	0
198	1	4.081889	6.127685	11.091126	4.812011	-0.005915	5.342211	0
199	1	0.985744	7.285737	-8.395940	-6.586471	-9.651765	6.651012	0

200 rows × 8 columns

data2_x=data2.iloc[:,:data2.shape[1]-1].values
data2_y=data2.iloc[:,data2.shape[1]-1].values

#第三個(gè)類(lèi)別的數(shù)據(jù)
data3=data.copy()
data3.loc[data["target"]==2,"target"]=1
data3.loc[data["target"]!=2,"target"]=0
data3

	ones	F1	F2	F3	F4	F5	F6	target
0	1	2.116632	7.972800	-9.328969	-8.224605	-12.178429	5.498447	1
1	1	1.886449	4.621006	2.841595	0.431245	-2.471350	2.507833	0
2	1	2.391329	6.464609	-9.805900	-7.289968	-9.650985	6.388460	1
3	1	-1.034776	6.626886	9.031235	-0.812908	5.449855	0.134062	0
4	1	-0.481593	8.191753	7.504717	-1.975688	6.649021	0.636824	0
...	...	...	...	...	...	...	...	...
195	1	5.434893	7.128471	9.789546	6.061382	0.634133	5.757024	0
196	1	-0.406625	7.586001	9.322750	-1.837333	6.477815	-0.992725	0
197	1	2.031462	7.804427	-8.539512	-9.824409	-10.046935	6.918085	1
198	1	4.081889	6.127685	11.091126	4.812011	-0.005915	5.342211	0
199	1	0.985744	7.285737	-8.395940	-6.586471	-9.651765	6.651012	1

200 rows × 8 columns

data3_x=data3.iloc[:,:data3.shape[1]-1].values
data3_y=data3.iloc[:,data3.shape[1]-1].values

#第四個(gè)類(lèi)別的數(shù)據(jù)
data4=data.copy()
data4.loc[data["target"]==3,"target"]=1
data4.loc[data["target"]!=3,"target"]=0
data4

	ones	F1	F2	F3	F4	F5	F6	target
0	1	2.116632	7.972800	-9.328969	-8.224605	-12.178429	5.498447	0
1	1	1.886449	4.621006	2.841595	0.431245	-2.471350	2.507833	0
2	1	2.391329	6.464609	-9.805900	-7.289968	-9.650985	6.388460	0
3	1	-1.034776	6.626886	9.031235	-0.812908	5.449855	0.134062	0
4	1	-0.481593	8.191753	7.504717	-1.975688	6.649021	0.636824	0
...	...	...	...	...	...	...	...	...
195	1	5.434893	7.128471	9.789546	6.061382	0.634133	5.757024	1
196	1	-0.406625	7.586001	9.322750	-1.837333	6.477815	-0.992725	0
197	1	2.031462	7.804427	-8.539512	-9.824409	-10.046935	6.918085	0
198	1	4.081889	6.127685	11.091126	4.812011	-0.005915	5.342211	1
199	1	0.985744	7.285737	-8.395940	-6.586471	-9.651765	6.651012	0

200 rows × 8 columns

data4_x=data4.iloc[:,:data4.shape[1]-1].values
data4_y=data4.iloc[:,data4.shape[1]-1].values

3. 定義假設(shè)函數(shù)、代價(jià)函數(shù)和梯度下降算法

def sigmoid(z):
    return 1 / (1 + np.exp(-z))

def h(X,w):
    z=X@w
    h=sigmoid(z)
    return h

#代價(jià)函數(shù)構(gòu)造
def cost(X,w,y):
    #當(dāng)X(m,n+1),y(m,),w(n+1,1)
    y_hat=sigmoid(X@w)
    right=np.multiply(y.ravel(),np.log(y_hat).ravel())+np.multiply((1-y).ravel(),np.log(1-y_hat).ravel())
    cost=-np.sum(right)/X.shape[0]
    return cost

def grandient(X,y,iter_num,alpha):
    y=y.reshape((X.shape[0],1))
    w=np.zeros((X.shape[1],1))
    cost_lst=[]  
    for i in range(iter_num):
        y_pred=h(X,w)-y
        temp=np.zeros((X.shape[1],1))
        for j in range(X.shape[1]):
            right=np.multiply(y_pred.ravel(),X[:,j])
            
            gradient=1/(X.shape[0])*(np.sum(right))
            temp[j,0]=w[j,0]-alpha*gradient
        w=temp
        cost_lst.append(cost(X,w,y.ravel()))
    return w,cost_lst

4. 學(xué)習(xí)這四個(gè)分類(lèi)模型

import matplotlib.pyplot as plt

#初始化超參數(shù)
iter_num,alpha=600000,0.001

#訓(xùn)練第1個(gè)模型
w1,cost_lst1=grandient(data1_x,data1_y,iter_num,alpha)

plt.plot(range(iter_num),cost_lst1,"b-o")

[<matplotlib.lines.Line2D at 0x25624eb08e0>]

python多分類(lèi)邏輯回歸,《 Python機(jī)器學(xué)習(xí)入門(mén)實(shí)驗(yàn) 》,機(jī)器學(xué)習(xí),python,分類(lèi)

#訓(xùn)練第2個(gè)模型
w2,cost_lst2=grandient(data2_x,data2_y,iter_num,alpha)
plt.plot(range(iter_num),cost_lst2,"b-o")

[<matplotlib.lines.Line2D at 0x25631b87a60>]

python多分類(lèi)邏輯回歸,《 Python機(jī)器學(xué)習(xí)入門(mén)實(shí)驗(yàn) 》,機(jī)器學(xué)習(xí),python,分類(lèi)

#訓(xùn)練第3個(gè)模型
w3,cost_lst3=grandient(data3_x,data3_y,iter_num,alpha)
plt.plot(range(iter_num),cost_lst3,"b-o")

[<matplotlib.lines.Line2D at 0x2562bcdfac0>]

python多分類(lèi)邏輯回歸,《 Python機(jī)器學(xué)習(xí)入門(mén)實(shí)驗(yàn) 》,機(jī)器學(xué)習(xí),python,分類(lèi)

#訓(xùn)練第4個(gè)模型
w4,cost_lst4=grandient(data4_x,data4_y,iter_num,alpha)
plt.plot(range(iter_num),cost_lst4,"b-o")

[<matplotlib.lines.Line2D at 0x25631ff4ee0>]

python多分類(lèi)邏輯回歸,《 Python機(jī)器學(xué)習(xí)入門(mén)實(shí)驗(yàn) 》,機(jī)器學(xué)習(xí),python,分類(lèi)

5. 利用模型進(jìn)行預(yù)測(cè)

data_x

array([[ 2.11663151e+00,  7.97280013e+00, -9.32896918e+00,
        -8.22460526e+00, -1.21784287e+01,  5.49844655e+00],
       [ 1.88644899e+00,  4.62100554e+00,  2.84159548e+00,
         4.31244563e-01, -2.47135027e+00,  2.50783257e+00],
       [ 2.39132949e+00,  6.46460915e+00, -9.80590050e+00,
        -7.28996786e+00, -9.65098460e+00,  6.38845956e+00],
       ...,
       [ 2.03146167e+00,  7.80442707e+00, -8.53951210e+00,
        -9.82440872e+00, -1.00469351e+01,  6.91808489e+00],
       [ 4.08188906e+00,  6.12768483e+00,  1.10911262e+01,
         4.81201082e+00, -5.91530191e-03,  5.34221079e+00],
       [ 9.85744105e-01,  7.28573657e+00, -8.39593964e+00,
        -6.58647097e+00, -9.65176507e+00,  6.65101187e+00]])

data_x=np.insert(data_x,0,1,axis=1)

data_x.shape

(200, 7)

w3.shape

(7, 1)

multi_pred=pd.DataFrame(zip(h(data_x,w1).ravel(),h(data_x,w2).ravel(),h(data_x,w3).ravel(),h(data_x,w4).ravel()))
multi_pred

	0	1	2	3
0	0.020436	4.556248e-15	9.999975e-01	2.601227e-27
1	0.820488	4.180906e-05	3.551499e-05	5.908691e-05
2	0.109309	7.316201e-14	9.999978e-01	7.091713e-24
3	0.036608	9.999562e-01	1.048562e-09	5.724854e-03
4	0.003075	9.999292e-01	2.516742e-09	6.423038e-05
...	...	...	...	...
195	0.017278	3.221293e-06	3.753372e-14	9.999943e-01
196	0.003369	9.999966e-01	6.673394e-10	2.281428e-03
197	0.000606	1.118174e-13	9.999941e-01	1.780212e-28
198	0.013072	4.999118e-05	9.811154e-14	9.996689e-01
199	0.151548	1.329623e-13	9.999447e-01	2.571989e-24

200 rows × 4 columns文章來(lái)源地址http://www.zghlxwxcb.cn/news/detail-628300.html

6. 計(jì)算準(zhǔn)確率

np.sum(np.argmax(multi_pred.values,axis=1)==data_y.ravel())/len(data)

1.0

附：系列文章

實(shí)驗(yàn)	目錄	直達(dá)鏈接
1	Numpy以及可視化回顧	https://want595.blog.csdn.net/article/details/131891689
2	線(xiàn)性回歸	https://want595.blog.csdn.net/article/details/131892463
3	邏輯回歸	https://want595.blog.csdn.net/article/details/131912053
4	多分類(lèi)實(shí)踐(基于邏輯回歸)	https://want595.blog.csdn.net/article/details/131913690
5	機(jī)器學(xué)習(xí)應(yīng)用實(shí)踐-手動(dòng)調(diào)參	https://want595.blog.csdn.net/article/details/131934812
6	貝葉斯推理	https://want595.blog.csdn.net/article/details/131947040
7	KNN最近鄰算法	https://want595.blog.csdn.net/article/details/131947885
8	K-means無(wú)監(jiān)督聚類(lèi)	https://want595.blog.csdn.net/article/details/131952371
9	決策樹(shù)	https://want595.blog.csdn.net/article/details/131991014
10	隨機(jī)森林和集成學(xué)習(xí)	https://want595.blog.csdn.net/article/details/132003451
11	支持向量機(jī)	https://want595.blog.csdn.net/article/details/132010861
12	神經(jīng)網(wǎng)絡(luò)-感知器	https://want595.blog.csdn.net/article/details/132014769
13	基于神經(jīng)網(wǎng)絡(luò)的回歸-分類(lèi)實(shí)驗(yàn)	https://want595.blog.csdn.net/article/details/132127413
14	手寫(xiě)體卷積神經(jīng)網(wǎng)絡(luò)	https://want595.blog.csdn.net/article/details/132223494
15	將Lenet5應(yīng)用于Cifar10數(shù)據(jù)集	https://want595.blog.csdn.net/article/details/132223751
16	卷積、下采樣、經(jīng)典卷積網(wǎng)絡(luò)	https://want595.blog.csdn.net/article/details/132223985

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【Python機(jī)器學(xué)習(xí)】決策樹(shù)、邏輯回歸、神經(jīng)網(wǎng)絡(luò)等模型對(duì)電信用戶(hù)流失分類(lèi)實(shí)戰(zhàn)（附源碼和數(shù)據(jù)集）
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