make_friedman1

您的问题似乎不太清楚，但我猜您可能是在问如何使用Python生成Friedman #1函数。如果是这样，以下是一个例子：

import numpy as np

def make_friedman1(n_samples=100, n_features=5, noise=0.1):
    X = np.random.randn(n_samples, n_features)
    y = (
        10 * np.sin(np.pi * X[:, 0] * X[:, 1])
        + 20 * (X[:, 2] - 0.5) ** 2
        + 10 * X[:, 3]
        + 5 * X[:, 4]
        + noise * np.random.randn(n_samples)
    )
    return X, y

这个函数可以生成一个Friedman #1数据集，其中包含100个样本，每个样本有5个特征，噪声为0.1。

相关问题

损失函数friedman_mse

Friedman's mean squared error (Friedman MSE) is a loss function used in gradient boosting regression algorithms. It is a modification of the regular mean squared error (MSE) that adds a penalty term to the residuals in order to reduce overfitting. The penalty term is proportional to the magnitude of the residual, but also takes into account the complexity of the model through a tuning parameter. By adding this penalty term, Friedman MSE encourages the model to select simpler trees with smaller residual errors.

The formula for Friedman MSE is:

MSE(y, f(x)) + α * T

where MSE is the regular mean squared error, y is the true target values, f(x) is the predicted target values, α is the tuning parameter that controls the strength of the penalty term, and T is the complexity of the model (measured as the number of terminal nodes in the decision tree).

Friedman MSE is commonly used in gradient boosting algorithms, such as XGBoost and LightGBM, as it helps to improve the model's generalization performance and prevent overfitting.

friedman检验

Friedman检验是一种多个相关样本齐一性的统计检验方法，也被称为弗里德曼双向秩方差分析。它的前提要求是顺序级数据、三个或更多组相关的小组，以及从搭配的数值中随机地抽取样本。该方法在1973年由弗里德曼提出。Friedman检验是非参数版的anova2，需要注意的是，它只注重其中一个因素对待检验数据的影响，而另一个因素则是用来区分区组用的。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
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