原创网址：https://blog.csdn.net/qq_27150893/article/details/80169736
sklearn是非常流行的机器学习库，实现了很多的机器学习模型。官网：http://scikit-learn.org/stable/ 里面有全面的实例和模型参数讲解，用到哪个模型就去官方查看说明文档。
基本功能主要被分为六大部分：分类，回归，聚类，数据降维，模型选择和数据预处理。
Estimator框架的基本使用套路：
model = EstimatorObject() #得到模型
model.fit(dataset.data, dataset.target) #训练模型
model.predict(dataser.data) #预测
本文对主要的机器学习模型进行实例演示，具体模型的参数结合的自己需求设置。
1.分类问题
数据集为 Car Ecaluation,根据汽车的若干属性对汽车性能进行评价。下载地址：http://archive.ics.uci.edu/ml/datasets/Car+Evaluation
预处理：将数据集保存后将后缀直接改为csv，并将里面用字符串表示的等级转化为数字。如small,low,unacc转化为1，2，3
1.1 SVM支持向量机模型
from sklearn import svmimport pandas as pdimport numpy as npfrom sklearn.model_selection import train_test_split#SVM模型实现汽车性能评测car_data = pd.read_csv(r'D:\pyproject\sklearn\car.csv')car_data = car_data.dropna() #去掉缺失值#提取特征和类别X= car_data.ix[:, :'safety']y= car_data.ix[:,'class']#划分训练集和测试集X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)# 建立模型。 设置算法内核类型，有 'linear’, ‘poly’, ‘rbf’, ‘sigmoid’;惩罚参数为1，一般为10的幂次方svc_model = svm.SVC(kernel='rbf', C= 1)svc_model.fit(X_train, y_train)predict_data = svc_model.predict(X_test)accuracy = np.mean(predict_data==y_test)print(accuracy)
运行结果：
1.2 MLP神经网络模型
from sklearn.neural_network import MLPClassifierimport pandas as pdimport numpy as npfrom sklearn.model_selection import train_test_split#MLP神经网络模型实现汽车性能评测car_data = pd.read_csv(r'D:\pyproject\sklearn\car.csv')car_data = car_data.dropna() #去掉缺失值#提取特征和对象类别X= car_data.ix[:, :'safety']y= car_data.ix[:,'class']#划分训练集和测试集X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)#建立MLP神经网络模型 ，MLP的求解方法为adam，可选lbfgs、sgd，正则化惩罚alpha = 0.1mpl_model = MLPClassifier(solver='adam', learning_rate='constant', learning_rate_init=0.01,max_iter = 500,alpha =0.01)mpl_model.fit(X_train, y_train)predict_data = mpl_model.predict(X_test)accuracy = np.mean(predict_data == y_test)print(accuracy)
运行结果：
1.3 逻辑回归模型
import pandas as pdimport numpy as npfrom sklearn.model_selection import train_test_splitfrom sklearn.linear_model import LogisticRegression#逻辑回归模型实现汽车性能预测car_data = pd.read_csv(r'D:\pyproject\sklearn\car.csv')car_data = car_data.dropna() #去掉缺失值#提取特征和对象类别X= car_data.ix[:, :'safety']y= car_data.ix[:, 'class']#划分训练集和测试集X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)#建立逻辑回归模型 ，惩罚参数为100lr_model = LogisticRegression(C= 100, max_iter=1000)lr_model.fit(X_train, y_train)predict_data = lr_model.predict(X_test)accuracy = np.mean(predict_data == y_test)print(accuracy)
运行结果：
1.4 决策树模型
from sklearn import treeimport pandas as pdimport numpy as npfrom sklearn.model_selection import train_test_split#决策树模型实现汽车性能预测car_data = pd.read_csv(r'D:\pyproject\sklearn\car.csv')car_data = car_data.dropna() #去掉缺失值#提取特征和类别X= car_data.ix[:, :'safety']y= car_data.ix[:,'class']#划分训练集和测试集X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)# 建立决策树模型，选择算法为熵增益，可选gini,entropy,默认为ginitree_model = tree.DecisionTreeClassifier(criterion='gini')tree_model.fit(X_train, y_train)predict_data = tree_model.predict(X_test)accuracy = np.mean(predict_data==y_test)print(accuracy)
运行结果：
1.5 KNN（K最临近模型）
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from sklearn import neighbors -
import pandas as pd -
import numpy as np -
from sklearn.model_selection import train_test_split -
#K最邻模型实现汽车性能预测 -
car_data = pd.read_csv(r'D:\pyproject\sklearn\car.csv') -
car_data = car_data.dropna() #去掉缺失值 -
#提取特征和类别 -
X= car_data.ix[:, :'safety'] -
y= car_data.ix[:, 'class'] -
#划分训练集和测试集 -
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0) -
# 建立KNN模型，邻居数选为7，默认为5 -
knn_model = neighbors.KNeighborsClassifier(n_neighbors = 7) -
knn_model.fit(X_train, y_train) -
#对测试集进行预测 -
predict_data = knn_model.predict(X_test) -
accuracy = np.mean(predict_data==y_test) -
print(accuracy)
运行结果：
2. 回归问题
这里使用sklearn自带的数据集，数据集为波斯顿房价，根据波斯顿地区若干指标对房价进行预测。
2.1 线性回归模型实现
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from sklearn.linear_model import LinearRegression -
from sklearn.datasets import load_boston -
from sklearn.model_selection import train_test_split -
#导入结果评价包 -
from sklearn.metrics import mean_absolute_error -
#利用线性回归模型预测波斯顿房价 -
#下载sklearn自带的数据集 -
data = load_boston() -
#建立线性回归模型 -
clf = LinearRegression() -
#划分训练集和测试集 -
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.3, random_state=0) -
clf.fit(X_train, y_train) -
predict_data = clf.predict(X_test) -
print(predict_data) -
#平均绝对值误差对结果进行评价 -
appraise = mean_absolute_error(y_test, predict_data) -
print(appraise)
运行结果：
