多分类以及机器学习实践
如何对多个类别进行分类
Iris数据集是常用的分类实验数据集，由Fisher, 1936收集整理。Iris也称鸢尾花卉数据集，是一类多重变量分析的数据集。数据集包含150个数据样本，分为3类，每类50个数据，每个数据包含4个属性。可通过花萼长度，花萼宽度，花瓣长度，花瓣宽度4个属性预测鸢尾花卉属于（Setosa，Versicolour，Virginica）三个种类中的哪一类。
iris以鸢尾花的特征作为数据来源，常用在分类操作中。该数据集由3种不同类型的鸢尾花的各50个样本数据构成。其中的一个种类与另外两个种类是线性可分离的，后两个种类是非线性可分离的。
该数据集包含了4个属性：
Sepal.Length（花萼长度），单位是cm;
Sepal.Width（花萼宽度），单位是cm;
Petal.Length（花瓣长度），单位是cm;
Petal.Width（花瓣宽度），单位是cm;
种类：Iris Setosa（山鸢尾）、Iris Versicolour（杂色鸢尾），以及Iris Virginica（维吉尼亚鸢尾）。
1.1 数据的预处理
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],
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[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 训练数据的准备
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))
#训练数据的特征和标签
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],
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[0],
[0],
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[0],
[0],
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[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],
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[2],
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[2],
[2],
[2],
[2],
[2],
[2],
[2],
[2],
[2],
[2],
[2],
[2],
[2],
[2],
[2],
[2],
[2]]))
由于有三个类别，那么在训练时三类数据要分开
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,))
#针对第二类，即第二个分类器的数据
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]
6
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
#针对第三类，即第三个分类器的数据
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 定义假设函数，代价函数，梯度下降算法（从实验3复制过来）
def sigmoid(z):
return 1 / (1 + np.exp(-z))
def h(X,w):
z=X@w
h=sigmoid(z)
return h
#代价函数构造
def cost(X,w,y):
#当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
#代价函数构造
def cost(X,w,y):
#当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 调用梯度下降算法来学习三个分类模型的参数
#初始化超参数
iter_num,alpha=600000,0.001
#训练第一个模型
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>]
#训练第二个模型
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>]
#训练第三个模型
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>]
1.5 利用模型进行预测
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]])
#将数据输入三个模型的看看结果
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]])
#每个样本的预测值
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)
#每个样本的真实值
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 评估模型
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())
145
np.sum(np.argmax(multi_pred.values,axis=1)==data_y.ravel())/len(data)
0.9666666666666667
1.7 试试sklearn
from sklearn.linear_model import LogisticRegression
#建立第一个模型
clf1=LogisticRegression()
clf1.fit(data1_x,data1_y)
#建立第二个模型
clf2=LogisticRegression()
clf2.fit(data2_x,data2_y)
#建立第三个模型
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)
#可视化各模型的预测结果
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
#判断预测结果
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])
#计算准确率
np.sum(np.argmax(multi_pred.values,axis=1)==data_y.ravel())/data.shape[0]
0.7333333333333333
实验：请动手完成你们第一个多分类问题，祝好运！完成下面代码
1. 数据读取
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. 训练数据的准备
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
#第一个类别的数据
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,))
#第二个类别的数据
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
#第三个类别的数据
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
#第四个类别的数据
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. 定义假设函数、代价函数和梯度下降算法
def sigmoid(z):
return 1 / (1 + np.exp(-z))
def h(X,w):
z=X@w
h=sigmoid(z)
return h
#代价函数构造
def cost(X,w,y):
#当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. 学习这四个分类模型
import matplotlib.pyplot as plt
#初始化超参数
iter_num,alpha=600000,0.001
#训练第1个模型
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>]
#训练第2个模型
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>]
#训练第3个模型
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>]
#训练第4个模型
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>]
文章来源：https://www.toymoban.com/news/detail-675405.html
5. 利用模型进行预测
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文章来源地址https://www.toymoban.com/news/detail-675405.html
6. 计算准确率
np.sum(np.argmax(multi_pred.values,axis=1)==data_y.ravel())/len(data)
1.0
附：系列文章
| 实验 | 目录 | 直达链接 |
|---|---|---|
| 1 | Numpy以及可视化回顾 | https://want595.blog.csdn.net/article/details/131891689 |
| 2 | 线性回归 | https://want595.blog.csdn.net/article/details/131892463 |
| 3 | 逻辑回归 | https://want595.blog.csdn.net/article/details/131912053 |
| 4 | 多分类实践(基于逻辑回归) | https://want595.blog.csdn.net/article/details/131913690 |
| 5 | 机器学习应用实践-手动调参 | https://want595.blog.csdn.net/article/details/131934812 |
| 6 | 贝叶斯推理 | https://want595.blog.csdn.net/article/details/131947040 |
| 7 | KNN最近邻算法 | https://want595.blog.csdn.net/article/details/131947885 |
| 8 | K-means无监督聚类 | https://want595.blog.csdn.net/article/details/131952371 |
| 9 | 决策树 | https://want595.blog.csdn.net/article/details/131991014 |
| 10 | 随机森林和集成学习 | https://want595.blog.csdn.net/article/details/132003451 |
| 11 | 支持向量机 | https://want595.blog.csdn.net/article/details/132010861 |
| 12 | 神经网络-感知器 | https://want595.blog.csdn.net/article/details/132014769 |
| 13 | 基于神经网络的回归-分类实验 | https://want595.blog.csdn.net/article/details/132127413 |
| 14 | 手写体卷积神经网络 | https://want595.blog.csdn.net/article/details/132223494 |
| 15 | 将Lenet5应用于Cifar10数据集 | https://want595.blog.csdn.net/article/details/132223751 |
| 16 | 卷积、下采样、经典卷积网络 | https://want595.blog.csdn.net/article/details/132223985 |
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