python - 是否有用于非线性回归的 scikit-learn/keras 函数？

Question

我试图根据其他参数预测一些参数，数据是 24 个输入和 1 个输出（595 天）。

我已经尝试创建一个具有10 倍交叉验证的神经网络，但它在训练中给出了 30% 到 15% 的误差，在测试中给出了 40% 的误差。

def create_model():
    model = Sequential()
    # Adding the input layer
    model.add(Dense(24, kernel_initializer='normal', activation='relu', input_shape=(24,)))
    # Adding the hidden layer
    model.add(Dense(50, kernel_initializer='normal', activation='relu'))
    model.add(Dense(50, kernel_initializer='normal', activation='relu'))
    model.add(Dense(50, kernel_initializer='normal', activation='relu'))
    model.add(Dense(50, kernel_initializer='normal', activation='relu'))
    model.add(Dense(50, kernel_initializer='normal', activation='relu'))
    model.add(Dense(50, kernel_initializer='normal', activation='relu'))
    model.add(Dense(50, kernel_initializer='normal', activation='relu'))
    model.add(Dense(50, kernel_initializer='normal', activation='relu'))
    model.add(Dense(1))
    # Compiling the RNN
    model.compile(optimizer='adam', loss='mean_absolute_percentage_error')

    return model

kf = KFold(n_splits = 10, shuffle = True)
Density = create_model()

不知道我怎样才能最小化错误？还是有回归功能？

标签： pythonkerasscikit-learnnon-linear-regression