国产无码综合区,色欲AV无码国产永久播放,无码天堂亚洲国产AV,国产日韩欧美女同一区二区

<u id="qref9"><xmp id="qref9">

<u id="qref9"></u>

<thead id="qref9"><strong id="qref9"></strong></thead>

<p id="qref9"><ins id="qref9"></ins></p>

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

2年前作者：Want595分類(lèi)：Toy博客閱讀(25)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 多分類(lèi)實(shí)踐(基于邏輯回歸)。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)是線性可分離的，后兩個(gè)種類(lèi)是非線性可分離的。

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,《機(jī)器學(xué)習(xí) 》,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,《機(jī)器學(xué)習(xí) 》,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,《機(jī)器學(xué)習(xí) 》,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,《機(jī)器學(xué)習(xí) 》,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,《機(jī)器學(xué)習(xí) 》,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,《機(jī)器學(xué)習(xí) 》,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,《機(jī)器學(xué)習(xí) 》,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-675405.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

到了這里，關(guān)于【Python機(jī)器學(xué)習(xí)】實(shí)驗(yàn)04 多分類(lèi)實(shí)踐(基于邏輯回歸)的文章就介紹完了。如果您還想了解更多內(nèi)容，請(qǐng)?jiān)谟疑辖撬阉鱐OY模板網(wǎng)以前的文章或繼續(xù)瀏覽下面的相關(guān)文章，希望大家以后多多支持TOY模板網(wǎng)！

本文來(lái)自互聯(lián)網(wǎng)用戶(hù)投稿，該文觀點(diǎn)僅代表作者本人，不代表本站立場(chǎng)。本站僅提供信息存儲(chǔ)空間服務(wù)，不擁有所有權(quán)，不承擔(dān)相關(guān)法律責(zé)任。如若轉(zhuǎn)載，請(qǐng)注明出處：如若內(nèi)容造成侵權(quán)/違法違規(guī)/事實(shí)不符，請(qǐng)點(diǎn)擊違法舉報(bào)進(jìn)行投訴反饋，一經(jīng)查實(shí)，立即刪除！

分享到：

領(lǐng)支付寶紅包贊助服務(wù)器費(fèi)用

機(jī)器學(xué)習(xí)實(shí)驗(yàn)1——樸素貝葉斯和邏輯回歸分類(lèi)Adult數(shù)據(jù)集
基于Adult數(shù)據(jù)集，完成關(guān)于收入是否大于50K的邏輯回歸分類(lèi)、樸素貝葉斯模型訓(xùn)練、測(cè)試與評(píng)估。認(rèn)識(shí)數(shù)據(jù) 14個(gè)特征變量如下 1個(gè)目標(biāo)變量：Income：50K 或 50K 填充缺失值（“ ？”）統(tǒng)計(jì)各類(lèi)型數(shù)據(jù)缺失個(gè)數(shù)如下，這三種缺失數(shù)據(jù)類(lèi)型均為離散型(discrete)，因此采用眾數(shù)填充較
2024年01月23日
瀏覽(26)
機(jī)器學(xué)習(xí)算法（一）: 基于邏輯回歸的分類(lèi)預(yù)測(cè)
邏輯回歸的介紹邏輯回歸（Logistic regression，簡(jiǎn)稱(chēng)LR）雖然其中帶有\(zhòng)\\"回歸\\\"兩個(gè)字，但邏輯回歸其實(shí)是一個(gè) 分類(lèi) 模型，并且廣泛應(yīng)用于各個(gè)領(lǐng)域之中。雖然現(xiàn)在深度學(xué)習(xí)相對(duì)于這些傳統(tǒng)方法更為火熱，但實(shí)則這些傳統(tǒng)方法由于其獨(dú)特的優(yōu)勢(shì)依然廣泛應(yīng)用于各個(gè)領(lǐng)域中。而對(duì)于
2024年01月15日
瀏覽(32)
【Python機(jī)器學(xué)習(xí)】實(shí)驗(yàn)03 邏輯回歸
在這一次練習(xí)中，我們將要實(shí)現(xiàn)邏輯回歸并且應(yīng)用到一個(gè)分類(lèi)任務(wù)。我們還將通過(guò)將正則化加入訓(xùn)練算法，來(lái)提高算法的魯棒性，并用更復(fù)雜的情形來(lái)測(cè)試它。本實(shí)驗(yàn)的數(shù)據(jù)包含兩個(gè)變量(評(píng)分1和評(píng)分2，可以看作是特征),某大學(xué)的管理者，想通過(guò)申請(qǐng)學(xué)生兩次測(cè)試的評(píng)分，來(lái)
2024年02月11日
瀏覽(30)
AI：04-基于機(jī)器學(xué)習(xí)的蘑菇分類(lèi)
?? 本文選自專(zhuān)欄：AI領(lǐng)域?qū)?從基礎(chǔ)到實(shí)踐，深入了解算法、案例和最新趨勢(shì)。無(wú)論你是初學(xué)者還是經(jīng)驗(yàn)豐富的數(shù)據(jù)科學(xué)家，通過(guò)案例和項(xiàng)目實(shí)踐，掌握核心概念和實(shí)用技能。每篇案例都包含代碼實(shí)例，詳細(xì)講解供大家學(xué)習(xí)。 ??????本專(zhuān)欄包含以下學(xué)習(xí)方向：機(jī)器學(xué)習(xí)、
2024年02月10日
瀏覽(5)
機(jī)器學(xué)習(xí)：基于邏輯回歸和高斯貝葉斯對(duì)人口普查數(shù)據(jù)集的分類(lèi)與預(yù)測(cè)
機(jī)器學(xué)習(xí)：基于邏輯回歸和高斯貝葉斯對(duì)人口普查數(shù)據(jù)集的分類(lèi)與預(yù)測(cè) 作者：i阿極作者簡(jiǎn)介：Python領(lǐng)域新星作者、多項(xiàng)比賽獲獎(jiǎng)?wù)撸翰┲鱾€(gè)人首頁(yè) ??????如果覺(jué)得文章不錯(cuò)或能幫助到你學(xué)習(xí)，可以點(diǎn)贊??收藏??評(píng)論??+關(guān)注哦！?????? ??????如果有小伙伴需要數(shù)據(jù)
2023年04月08日
瀏覽(21)
python機(jī)器學(xué)習(xí)——分類(lèi)模型評(píng)估 & 分類(lèi)算法（k近鄰，樸素貝葉斯，決策樹(shù)，隨機(jī)森林，邏輯回歸，svm）
交叉驗(yàn)證：為了讓被評(píng)估的模型更加準(zhǔn)確可信交叉驗(yàn)證：將拿到的數(shù)據(jù)，分為訓(xùn)練和驗(yàn)證集。以下圖為例：將數(shù)據(jù)分成5份，其中一份作為驗(yàn)證集。然后經(jīng)過(guò)5次(組)的測(cè)試，每次都更換不同的驗(yàn)證集。即得到5組模型的結(jié)果，取平均值作為最終結(jié)果。又稱(chēng)5折交叉驗(yàn)證。通常情
2024年02月03日
瀏覽(31)
【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）
需要源碼和數(shù)據(jù)集請(qǐng)點(diǎn)贊關(guān)注收藏后評(píng)論區(qū)留言私信~~~ 該實(shí)例數(shù)據(jù)來(lái)自kaggle，它的每一條數(shù)據(jù)為一個(gè)用戶(hù)的信息，共有21個(gè)有效字段，其中最后一個(gè)字段Churn標(biāo)志該用戶(hù)是否流失 ? 可用pandas的read_csv()函數(shù)來(lái)讀取數(shù)據(jù)，用DataFrame的head()、shape、info()、duplicated()、nunique()等來(lái)初步
2024年02月03日
瀏覽(29)
【機(jī)器學(xué)習(xí)】邏輯回歸（二元分類(lèi)）
離散感知器：輸出的預(yù)測(cè)值僅為 0 或 1 連續(xù)感知器（邏輯分類(lèi)器）：輸出的預(yù)測(cè)值可以是 0 到 1 的任何數(shù)字，標(biāo)簽為 0 的點(diǎn)輸出接近于 0 的數(shù)，標(biāo)簽為 1 的點(diǎn)輸出接近于 1 的數(shù) 邏輯回歸算法（logistics regression algorithm）：用于訓(xùn)練邏輯分類(lèi)器的算法 sigmoid 函數(shù)： g ( z ) = 1 1 +
2024年02月21日
瀏覽(24)
【機(jī)器學(xué)習(xí)】鳶尾花分類(lèi)-邏輯回歸示例
功能：這段代碼演示了如何使用邏輯回歸對(duì)鳶尾花數(shù)據(jù)集進(jìn)行訓(xùn)練，并將訓(xùn)練好的模型保存到文件中。然后，它允許用戶(hù)輸入新的鳶尾花特征數(shù)據(jù)，使用保存的模型進(jìn)行預(yù)測(cè)，并輸出預(yù)測(cè)結(jié)果。步驟概述：加載數(shù)據(jù)和預(yù)處理：使用 Scikit-Learn 中的 datasets 模塊加載鳶尾花數(shù)據(jù)
2024年02月10日
瀏覽(25)
【白話機(jī)器學(xué)習(xí)的數(shù)學(xué)】讀書(shū)筆記(3)學(xué)習(xí)分類(lèi)(感知機(jī)、邏輯回歸)
1.分類(lèi)的目的找到一條線把白點(diǎn)和黑點(diǎn)分開(kāi)。這條直線是使權(quán)重向量成為法線向量的直線。(解釋見(jiàn)下圖) 直線的表達(dá)式為： ω ? x = ∑ i = 1 n ω i ? x i = 0 omega·x = sum_{i=1}^nomega_i · x_i = 0 ω ? x = i = 1 ∑ n ? ω i ? ? x i ? = 0 ω omega ω 是權(quán)重向量權(quán)重向量就是我們想要知
2024年01月18日
瀏覽(28)

<p id="lgumg"></p>

<ol id="lgumg"><pre id="lgumg"></pre></ol>

<rp id="lgumg"><dfn id="lgumg"><code id="lgumg"></code></dfn></rp>