如何在 Tensorflow 中计算 R^2 [英] How to Calculate R^2 in Tensorflow

问题描述

我正在尝试在 Tensorflow 中进行回归.我不确定我是否正确计算了 R^2，因为 Tensorflow 给了我与 sklearn.metrics.r2_score 不同的答案有人可以看看我下面的代码，让我知道我是否实现了图中的方程正确.谢谢

I am trying to do regression in Tensorflow. I'm not positive I am calculating R^2 correctly as Tensorflow gives me a different answer than sklearn.metrics.r2_score Can someone please look at my below code and let me know if I implemented the pictured equation correctly. Thanks

total_error = tf.square(tf.sub(y, tf.reduce_mean(y)))
unexplained_error = tf.square(tf.sub(y, prediction))
R_squared = tf.reduce_mean(tf.sub(tf.div(unexplained_error, total_error), 1.0))
R = tf.mul(tf.sign(R_squared),tf.sqrt(tf.abs(R_squared)))

推荐答案

你在计算什么R^2"是

What you are computing the "R^2" is

与给定的表达式相比，您在错误的位置计算平均值.在进行除法之前，您应该在计算误差时取平均值.

compared to the given expression, you are computing the mean at the wrong place. You should take the mean when computing the errors, before doing the division.

unexplained_error = tf.reduce_sum(tf.square(tf.sub(y, prediction)))
total_error = tf.reduce_sum(tf.square(tf.sub(y, tf.reduce_mean(y))))
R_squared = tf.sub(1, tf.div(unexplained_error, total_error))

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