博雅数据机器学习06

博雅数据机器学习06

线性回归

from sklearn import linear_model

from numpy import mat, array, mean

 

# 根据X和y训练模型并计算预测值y_pred

X = insurance.drop(['charges'], axis=1)

y = insurance['charges']

ws = linearRegression(X, y)

y_pred = mat(X.values)*ws

y_pred = array(y_pred).reshape(y_pred.shape[0],) # 将矩阵转换为一行多列的array格式

 

# 自定义决定系数函数，并对训练得到的模型进行评价

def r2_Score(y_true, y_pred):

    sst = sum((y_true-mean(y_true))**2)

    ssr = sum((y_true-y_pred)**2)

    sse = sum((y_pred-mean(y_true))**2)

    r2 = 1-float(ssr)/sst

    return round(r2,2)

score = r2_Score(y, y_pred)

print(score)

 

# sklearn模型训练结果

from sklearn import linear_model, metrics

regr = linear_model.LinearRegression(fit_intercept=False)

regr.fit(X, y)

y_pred = regr.predict(X)

score_sklearn = round(metrics.r2_score(y, y_pred),2)

print(score_sklearn)