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程序问答发布时间：2022-06-02 发布网站：大佬教程 code.js-code.com

大佬教程收集整理的这篇文章主要介绍了具有 GridSearch 和递归特征消除的套索回归模型以获得最佳参数集？，大佬教程大佬觉得挺不错的，现在分享给大家，也给大家做个参考。

如何解决具有 GridSearch 和递归特征消除的套索回归模型以获得最佳参数集？？

开发过程中遇到具有 GridSearch 和递归特征消除的套索回归模型以获得最佳参数集？的问题如何解决？下面主要结合日常开发的经验，给出你关于具有 GridSearch 和递归特征消除的套索回归模型以获得最佳参数集？的解决方法建议，希望对你解决具有 GridSearch 和递归特征消除的套索回归模型以获得最佳参数集？有所启发或帮助；

我正在处理一个连续数的回归任务，我尝试了 GrIDSearch 的 lasso 回归模型

这是我得到的

Alpha = 0.000011 最高 r^2score = 0.80 代码拟合模型



for ind,i in enumerate(lambdas):    
    reg = Lasso(Alpha = i)
    reg.fit(normalized_x_Train,y_Train)
    results = cross_val_score(reg,normalized_x_Train,y_Train,cv=4,scoring="r2") 
    Train_r_squared[ind] = reg.score(normalized_x_Train,y_Train)    
    test_r_squared[ind] = reg.score(normalized_x_test,y_test)

df_lam = pd.DataFrame(test_r_squared*100,columns=['R_squared'])
df_lam['lambda'] = (lambdas)
best_lambda_cv = df_lam.loc[df_lam['R_squared'].IDxmax()][1]

model_cv = Lasso(Alpha=best_lambda_cv,max_iter=50000,fit_intercept=True,normalize=false,tol=0.0001,copy_X=True,positive=false,random_state=None,SELEction='cyclic')
model_cv.fit(normalized_x_Train,y_Train)

但是它们在回归模型上显示了许多特征，我想找到可能会增加或（有点）减少 r^2 的最佳特征集

所以我尝试了递归特征消除，代码如下

# get a List of models to evaluate
def get_models():
    models = Dict()
    for i in range(2,41):
        rfe = RFE(Lasso(Alpha=0.00001663157894736842,SELEction='cyclic'),n_features_to_SELEct=i)
        model = Lasso()
        model.fit(X_Train,y_Train)
        models[str(i)] = Pipeline(steps=[('s',rfE),('m',model)])
    return models

# evaluate a given model using cross-validation
def evaluate_model(model,X,y):
    scores = cross_val_score(model,y,scoring='r2',n_jobs=-1)
    return scores

# get the models to evaluate
models = get_models()

# evaluate the models and store results
results,names = List(),List()
for name,model in models.items():
    scores = evaluate_model(model,X_Train,y_Train)
    results.append(scores)
    names.append(Name)
    print('>%s %.3f' % (name,mean(scores)))

# plot model perfoRMANce for comparison
pyplot.boxplot(results,labels=names,showmeans=TruE)
pyplot.show()