如何可视化此类信息
我正在训练逻辑回归算法，它会为每次迭代返回以下信息。我将这些实体收集为整个分类的数组。
你能建议我一些可视化它的方法吗？例如，绘制损失与准确率的图是否合适？或者我应该使用什么样的图形类型？
***** Iteration #74 *****
Loss: 170.07
Feature L2-norm: 12.5714
Learning rate (eta): 0.00778819
Total number of feature updates: 236800
Loss variance: 5.01839
Seconds required for this iteration: 0.01
Accuracy: 0.9800 (784/800)
Micro P, R, F1: 0.9771 (384/393), 0.9821 (384/391), 0.9796
***** Iteration #75 *****
Loss: 166.81
Feature L2-norm: 12.4385
Learning rate (eta): 0.00769234
Total number of feature updates: 240000
Loss variance: 4.68113
Seconds required for this iteration: 0.01
Accuracy: 0.9800 (784/800)
Micro P, R, F1: 0.9771 (384/393), 0.9821 (384/391), 0.9796
I am training a logistic regression algorithm and it returns me the following information for each iteration. I am collecting these entities as arrays for the entire classification.
Can you suggest me some ways to visualize it? For example, is it appropriate to plot loss vs accuracy? Or what kind of graphic type I should use?
***** Iteration #74 *****
Loss: 170.07
Feature L2-norm: 12.5714
Learning rate (eta): 0.00778819
Total number of feature updates: 236800
Loss variance: 5.01839
Seconds required for this iteration: 0.01
Accuracy: 0.9800 (784/800)
Micro P, R, F1: 0.9771 (384/393), 0.9821 (384/391), 0.9796
***** Iteration #75 *****
Loss: 166.81
Feature L2-norm: 12.4385
Learning rate (eta): 0.00769234
Total number of feature updates: 240000
Loss variance: 4.68113
Seconds required for this iteration: 0.01
Accuracy: 0.9800 (784/800)
Micro P, R, F1: 0.9771 (384/393), 0.9821 (384/391), 0.9796
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我认为你不应该想象这些信息。您可以看到的是，L2 范数随着时间的推移而减小（因为它是目标最小化函数）并且准确性增加。但由于 F1 如此之高，我认为它是评估训练数据的指标。
因此，我建议对测试数据（不用于训练的数据）进行 Micro P、R、F1：0.9771 (384/393)、0.9821 (384/391)、0.9796 等报告，并创建迭代与 F1 的关系图。然后您将看到何时真正开始按图上的峰值过度拟合数据。
I' dont think taht you should visualize this information. All what you could see is that L2 norm is decreased over time(since it is target minimsation function) and accuracy increased. But since F1 is so high I think it is metrics for evaluation on training data.
So I would recommend to do Micro P, R, F1: 0.9771 (384/393), 0.9821 (384/391), 0.9796 such report on test data(data wich is not used for training) and create plot of iteration vs F1. And then you will see when you actually start overfitting data by peak on the plot.