np.linspace(0,40).reshape(-1,1) plt.plot(x_test,model.predict(x_test)) 有图可我不知道方程的参数 都有model了怎么能不知道 model.coef...不就是点到直线的距离的平方越小越好 np.sum(np.square(model.predict(x1) - y1)) 16.63930773735106 # 这个不错,挺小的 我不信是最好的,那就稍稍加一点截距0.01 y2 = model.coef...plt.figure(figsize=(10,10)) plt.scatter(x1,y1) plt.plot(x1,model.predict(x1),color = 'r') plt.plot(x1 , model.coef
²,再看看系数 >>> print('intercept:', model.intercept_) intercept: 5.633333333333329 >>> print('slope:', model.coef...13.73333333 19.13333333 24.53333333 29.93333333 35.33333333] 当然也可以使用下面的方法 >>> y_pred = model.intercept_ + model.coef...0.8615939258756776 >>> print('intercept:', model.intercept_) intercept: 5.52257927519819 >>> print('slope:', model.coef...>>> print('intercept:', model.intercept_) intercept: 21.372321428571425 >>> print('coefficients:', model.coef
train_test_split(x, y) #得到训练和测试训练集model = LinearRegression() #导入线性回归model.fit(x_train, y_train) # model.coef...得到三个系数 array([ 0.04480311, 0.19277245, -0.00301245]) 3.0258997429585506 for i in zip(x_train.columns, model.coef
np.linspace(0, 10, 1000) yfit = model.predict(xfit[:, np.newaxis]) print "Model slope: ", model.coef
load_data() model = LogisticRegression() model.fit(x_train, y_train) print("w: ", model.coef...y_train, y_test = load_data() model = LogisticRegression() model.fit(x_train, y_train) print("w: ", model.coef
y) plt.show() 第五步:数据建模 model = linear_model.LinearRegression() model.fit(x,y) 第六步:模型评估 model_coef = model.coef
) #准备要预测的数据:距海边65、12、44km的城市温度 y = model.predict(x)#结果:[33.56614386 30.32827794 32.28321585] print(model.coef
获取模型参数 拟合完成后,我们可以获取模型的参数,即斜率和截距: slope = model.coef_[0] intercept = model.intercept_ 6.
LinearRegression() 拟合模型 接下来,我们使用训练数据拟合模型: model.fit(X, y) 获取模型参数 拟合完成后,我们可以获取模型的参数,即斜率和截距: slope = model.coef
lasso_path(X, y, eps=eps) alphas_lasso = np.array([model.alpha for model in models]) coefs_lasso = np.array([model.coef...alphas_positive_lasso = np.array([model.alpha for model in models]) coefs_positive_lasso = np.array([model.coef...eps, l1_ratio=0.8) alphas_enet = np.array([model.alpha for model in models]) coefs_enet = np.array([model.coef...alphas_positive_enet = np.array([model.alpha for model in models]) coefs_positive_enet = np.array([model.coef
color='blue') plt.xlabel('气温') plt.ylabel('销售量') plt.show() print("截距与斜率:",model.intercept_,model.coef
) model.fit(dummy_train_df,y_train) cv_ridge.append(rmse_cv(model).mean()) coefs.append(model.coef...) model.fit(dummy_train_df,y_train) cv_lasso.append(rmse_cv(model).mean()) coefs.append(model.coef...precompute=False, random_state=None, selection='cyclic', tol=0.0001, warm_start=False) coef = pd.Series(model.coef
train_test_split(x, y) #得到训练和测试训练集 model = LinearRegression() #导入线性回归 model.fit(x_train, y_train) model.coef...0.19277245, -0.00301245]) model.intercept_ # 截距 3.0258997429585506 打印对应的参数 for i in zip(x_train.columns, model.coef
LogisticRegression(solver='liblinear') model.fit(train_x,train_y) # In[34]: #训练模型的系数 print('Coefficient of model :', model.coef
the model model = LinearRegression() # fit the model model.fit(X, y) # get importance importance = model.coef...the model model = LogisticRegression() # fit the model model.fit(X, y) # get importance importance = model.coef
LinearRegression() model1.fit(x, y) print(model1.coef_) model = Ridge(alpha=1) model.fit(x, y) print(model.coef
plt.scatter(x,y) x_test = np.linspace(0,40).reshape(-1,1) plt.plot(x_test,model.predict(x_test)) 可以通过打印model.coef..._和model.coef_查斜率和截距,具体代码如下。...# 斜率 print(model.coef_) # 截距 print(model.intercept_) ####输出如下#### array([[1.00116024]]) array...y_train) #通过LinearRegression的coef_属性获得权重向量,intercept_获得b的值 print("权重向量(斜率):%s, 截距的值为:%.2f" % (model.coef
data model.fit(train_x,train_y) # coefficeints of the trained model print('\nCoefficient of model :', model.coef
linear_model.LinearRegression() model.fit(one_hot_df[['city_NYC', 'city_SF', 'city_Seattle']], one_hot_df[['Rent']]) model.coef...['Rent']) 输出:LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False) print(model.coef...['Rent']) 输出:LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False) print(model.coef
print('模型的均方误差值:', mean_squared_error(y, y_pred)) print('b0的值:', model.intercept_) print('b1的值:', model.coef
领取专属 10元无门槛券
手把手带您无忧上云