Text(0.5,1“Cost function using Gradient Descent')中的参数表示什么意思
时间: 2024-05-24 19:14:28 浏览: 17
在 "Cost function using Gradient Descent" 这个标题中,参数指的是函数中的变量或参数,可以是输入或输出。在这个标题中,可能的参数包括成本函数,梯度下降算法的学习率等等。其中,成本函数是一个函数,用于衡量模型预测结果与实际结果之间的误差大小,而梯度下降算法的学习率是控制参数更新速度的重要参数。在这个标题中,我们可能还会涉及其他的参数,具体取决于所讨论的问题和算法。
相关问题
plt.plot(J_history) plt.xlabel("Iteration") plt.ylabel("$J(\Theta)$") plt.title("Cost function using Gradient Descent")
这段代码使用了 Python 中的 Matplotlib 库来绘制梯度下降算法的损失函数 J(Theta) 随迭代次数的变化曲线图。具体来说,plt.plot(J_history) 语句将 J_history 中保存的每次迭代的损失函数值绘制在图像上,plt.xlabel("Iteration") 和 plt.ylabel("$J(\\Theta)$") 分别设置了 X 轴和 Y 轴的标签,plt.title("Cost function using Gradient Descent") 则设置了图像的标题。注意到 $J(\Theta)$ 使用了 Latex 语法,通过转义符号 \\ 可以将 $ 字符正确地显示在图像上。
robust cost function
In machine learning, a robust cost function is a way to measure the difference between the predicted output and the true output in a way that is less sensitive to outliers or errors in the data. This is particularly important when dealing with noisy or inconsistent data, where traditional cost functions like mean squared error may not be effective.
One example of a robust cost function is the Huber loss function. This function combines the advantages of both mean squared error and absolute error, by using a quadratic loss for small errors and a linear loss for larger errors. This makes it less sensitive to outliers than mean squared error alone, while still being differentiable and suitable for optimization algorithms like gradient descent.
Another example is the Tukey's biweight loss function, which is a type of M-estimator. This function is defined as a truncated parabolic function that gives zero weight to outliers beyond a certain threshold. This makes it highly robust to outliers while still being differentiable and computationally efficient.
Robust cost functions are particularly useful in applications like regression, where the goal is to predict a continuous value. By using a more robust cost function, the model can better handle noisy or inconsistent data, leading to more accurate predictions and better performance overall.
相关推荐
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![-](https://csdnimg.cn/download_wenku/file_type_column_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)