Eye
1. Introduction to the iris data set
The Iris flower data set is a classic multi-dimensional data set introduced by Sir Ronald Fisher, which can be used as a sample for discriminant analysis.
The data set contains 50 samples of each of the three varieties of Iris flowers (Iris setosa, Iris virginica and Iris versicolor), each sample There are also 4 characteristic parameters (respectively the length and width of the sepals and the length and width of the petals, in centimeters)
Two, logistic regression
1. Logistic Regression (Logistic Regression) is used to deal with the regression problem where the dependent variable is a categorical variable. The common one is binary classification or binomial distribution problem, and it can also handle multi-classification problems. It is a classification method.
2. Use of LogisticRegression regression model in Sklearn
Import the model
from sklearn.linear_model import LogisticRegression #导入逻辑回归模型
fit() training
clf = LogisticRegression()
print(clf)
clf.fit(train_feature,label)
predict() prediction
predict['label'] = clf.predict(predict_feature)
Three, linear multiple classification
Take the length and width of the sepals as features to classify
Import package
#导入相关包
import numpy as np
from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt
import matplotlib as mpl
from sklearn import datasets
from sklearn import preprocessing
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
Get the data set
# 获取所需数据集
iris=datasets.load_iris()
#每行的数据，一共四列，每一列映射为feature_names中对应的值
X=iris.data
print(X)
#每行数据对应的分类结果值（也就是每行数据的label值）,取值为[0,1,2]
Y=iris.target
print(Y)
data processing
#归一化处理
X = StandardScaler().fit_transform(X)
print(X)
Training model
lr = LogisticRegression() # Logistic回归模型
lr.fit(X, Y) # 根据数据[x,y]，计算回归参数
Draw classification images
N, M = 500, 500 # 横纵各采样多少个值
x1_min, x1_max = X[:, 0].min(), X[:, 0].max() # 第0列的范围
x2_min, x2_max = X[:, 1].min(), X[:, 1].max() # 第1列的范围
t1 = np.linspace(x1_min, x1_max, N)
t2 = np.linspace(x2_min, x2_max, M)
x1, x2 = np.meshgrid(t1, t2) # 生成网格采样点
x_test = np.stack((x1.flat, x2.flat), axis=1) # 测试点
cm_light = mpl.colors.ListedColormap(['#77E0A0', '#FF8080', '#A0A0FF'])
cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b'])
y_hat = lr.predict(x_test) # 预测值
y_hat = y_hat.reshape(x1.shape) # 使之与输入的形状相同
plt.pcolormesh(x1, x2, y_hat, cmap=cm_light) # 预测值的显示
plt.scatter(X[:, 0], X[:, 1], c=Y.ravel(), edgecolors='k', s=50, cmap=cm_dark)
plt.xlabel('petal length')
plt.ylabel('petal width')
plt.xlim(x1_min, x1_max)
plt.ylim(x2_min, x2_max)
plt.grid()
plt.show()
Predictive model
y_hat = lr.predict(X)
Y = Y.reshape(-1)
result = y_hat == Y
print(y_hat)
print(result)
acc = np.mean(result)
print('准确度: %.2f%%' % (100 * acc))
Take the length and width of the petals as features to classify in the
same way
in conclusion:. . . . .