Ridge.fit() got an unexpected keyword argument 'feature_names'

The error message you are seeing suggests that the `fit()` method in your Ridge regression model is not recognizing the `feature_names` argument that you are using. This may be because the `feature_names` argument is not a valid argument for the `fit()` method of the Ridge regression class.

To resolve this issue, you can try removing the `feature_names` argument from the `fit()` method call. Alternatively, you can try using a different regression model that supports the `feature_names` argument, or you can modify the code of the Ridge regression class to support the `feature_names` argument.

相关问题

from sklearn.linear_model import Ridge ridge = Ridge(alpha=1.0) ridge.fit(X_train[features_without_ones],y_train) w_ridge = [] w_ridge.extend(ridge.coef_) w_ridge.append(ridge.intercept_) w1["ridge_sklearn_w"] = w_ridge w1.round(decimals=2)

这段代码使用了scikit-learn库中的Ridge回归模型来拟合特征矩阵X_train和目标变量y_train，并将得到的系数和截距存储在列表w_ridge中。最后，将系数和截距放入字典w1中，键为"ridge_sklearn_w"。函数round(decimals=2)是将结果四舍五入到小数点后两位。Ridge回归是一种线性回归模型，其中加入了L2正则化项，可以用来处理多重共线性问题。

#模型预测 y_pred = model.predict(X_test) accuracy = accuracy_score(y_test, y_pred) error = 1 - accuracy print("错误率为：", f'{error*100:.2f}%') #在测试集上进行预测 y_pred = ir.predict(X_test)#predict()函数是Python中预测函数，常用于预测测试集数据，返回预测数据 accuracy = accuracy_score(y_test, y_pred) error = 1 - accuracy print("错误率为：", f'{error*100:.2f}%') y_pred = reg.predict(X_test) reg.coef_ scores = cross_val_score(reg, X_train, y_train, cv=5) print("一般线性回归（R2平均值错误率：{:.3f}，R2标准差：{:.3f}）".format(np.mean(scores), np.std(scores))) #岭回归(L1) ridge = linear_model.Ridge(alpha=0.5) ridge.fit(X_train, y_train) # 训练模型 ridge.fit(X_train, y_train) #预测测试数据集 y_pred_ridge = ridge.predict(X_test) reg.coef_ reg.intercept_ scores = cross_val_score(reg, X_train, y_train, cv=5) print("岭回归（R2平均值错误率：{:.3f}，R2标准差：{:.3f}）".format(np.mean(scores), np.std(scores))) #Lasso回归(L2) lasso = linear_model.Lasso(alpha=0.1) lasso.fit(X_train, y_train) # 训练模型 lasso.fit(X_train, y_train) # 预测测试数据集 y_pred_lasso = lasso.predict(X_test) scores = cross_val_score(reg, X_train, y_train, cv=5) print("Lasso回归（R2平均值错误率：{:.3f}，R2标准差：{:.3f}）".format(np.mean(scores), np.std(scores)))

这段代码是用于模型预测的一些操作。首先，它使用模型对测试集进行预测，并计算准确率和错误率。然后，它使用不同的回归模型（一般线性回归、岭回归和Lasso回归）对测试集进行预测，并计算R2平均值和标准差作为评估指标。

这段代码展示了在测试集上使用不同的模型进行预测，并计算了准确率、错误率以及回归模型的评估指标。这些操作用于评估模型的性能和泛化能力，以选择最适合数据集的模型。