机器学习步骤

机器学习的步骤一般为加载数据集、分割数据集、训练模型、验证模型精度

鸢尾花data建立python机器学习

本次运行Python版本为3.6.2，且已安装相关python库，参考博客python版本3以下，故做了适应性修改。运行方式为在python命令执行代码，或下载代码到本地目录，windows dos窗口或linux shell客户端，进入代码所在目录执行
python mln_20181231.py
代码如下所示：

from sklearn.datasets import load_iris
from sklearn import model_selection

from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC

from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report

#load data
dataset = load_iris()

print(dataset.data.shape)
print(dataset.feature_names)

validation_size = 0.20 
seed = 7 
#分割数据集
X_train, X_validation, Y_train, Y_validation=model_selection.train_test_split(dataset.data, dataset.target,test_size=validation_size, random_state=seed)

scoring = 'accuracy' 
models = []
models.append(('LR', LogisticRegression())) 
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('CART', DecisionTreeClassifier())) 
models.append(('NB', GaussianNB()))
models.append(('SVM', SVC())) 
results = [] 
names = [] 
kfold = model_selection.KFold(n_splits=10, random_state=seed)

for name, model in models:
    kfold = model_selection.KFold(n_splits=10, random_state=seed)
    cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)#每一个算法模型作为其中的参数，计算每一模型的精度得分
    results.append(cv_results)
    names.append(name)
    msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std()) 
    print(msg) 
#SVM: 0.991667 (0.025000)
knn = KNeighborsClassifier()
#knn拟合训练集
knn.fit(X_train, Y_train)
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
           metric_params=None, n_jobs=None, n_neighbors=5, p=2,
           weights='uniform')
predictions = knn.predict(X_validation)
print(accuracy_score(Y_validation, predictions))#验证集精度得分

print(confusion_matrix(Y_validation, predictions))

print(classification_report(Y_validation, predictions))

参考

用鸢尾花data建立python机器学习的初步印象（一）
用鸢尾花data建立python机器学习代码
转：iris数据集及简介