I will be using the Historical Tornado Tracks dataset from the National Oceanic and Atmospheric Administration (NOAA) and the National Weather Service (NWS) to predict a tornado’s F-scale rating. The data I am using can be found here.

我将使用美国国家海洋和大气管理局(NOAA)和美国国家气象局(NWS)的“历史龙卷风轨迹”数据集来预测龙卷风的F等级定级。 我正在使用的数据可以在这里找到。

First, lets discuss what the F-scale is. Also called the Fujita scale, the F-scale rating was introduced in 1971 by Ted Fujita and Allen Pearson. It was designed as a way to measure the impact a tornado had on man-made structures and vegetation. It is not a method of measuring a tornado’s width, path length, or wind speed.

首先，让我们讨论一下F标尺。 F等级也称为Fujita量表，由Ted Fujita和Allen Pearson于1971年推出。 它被设计用来衡量龙卷风对人造结构和植被的影响。 它不是测量龙卷风的宽度，路径长度或风速的方法。

While the original scale had 13 theoretical categories, Fujita intended for only six to be used. These range from F0-F5 on the scale. The categories and their descriptions can be seen below:

最初的量表具有13个理论类别，而藤田只打算使用6个。 范围从F0-F5。 类别及其描述如下所示：

Now that we’re all familiar with the rating system, I’ll dive into the data. While the dataset includes all US tornadoes from 1950–2018, tornadoes from 1950–1972 were retroactively rated by NOAA. Included are features describing starting and ending latitudes and longitudes, fatalities, injuries, loss and crop loss (in millions of dollars), path length, path width, and more.

现在我们都熟悉评分系统，我将深入研究数据。 虽然数据集包含1950-2018年的所有美国龙卷风，但NOAA追溯评估了1950-1972年的龙卷风。 其中包括描述起始和结束纬度和经度，死亡人数，伤害，损失和农作物损失(百万美元)，路径长度，路径宽度等的功能。

First, let’s look at the distribution of F-ratings in the data:

首先，让我们看一下数据中F等级的分布：

As you can see in the image above, the data has very imbalanced classes. Over 46% of all tornadoes since 1950 are classified as F0's, while not even 0.1% of them were F5’s. This will introduce several challenges with classification which I will go over later.

如上图所示，数据具有非常不平衡的类。 自1950年以来，所有龙卷风中超过46％被归类为F0，而其中甚至没有0.1％是F5。 这将为分类带来一些挑战，我将在稍后进行介绍。

To start any classification problem, let’s determine our baseline. We’ll use the majority class as our baseline, which, as we can see in the distribution charts, is 46 percent. For those not familiar with classification, all this means is that if we guessed that every entry in the dataset was an F0 (the majority class), we would be correct almost half of the time. So how can we improve our guess rate?

要开始任何分类问题，让我们确定基线。 我们将以多数类作为基准，正如我们在分布图中看到的那样，该基准为46％。 对于那些不熟悉分类的人来说，这一切意味着，如果我们猜测数据集中的每个条目都是F0(多数类)，那么几乎一半的时间我们都是正确的。 那么我们如何提高猜测率呢？

Let’s move on to a simple model: Random Forest classifier (RF). One thing to note is that at this point I have already done some feature selection to produce a better result out of the Random Forest model. More on that in a minute.

让我们继续一个简单的模型：随机森林分类器(RF)。 需要注意的一件事是，在这一点上，我已经进行了一些功能选择，以根据“随机森林”模型产生更好的结果。 一分钟内可以了解更多。

So running a RF model on the data, we get this confusion matrix. More info about confusion matrices can be found here. The accuracy we get from this model is 66 percent, a significant improvement over our baseline of 46 percent.

因此，对数据运行RF模型，我们得到了这个混淆矩阵。 有关混乱矩阵的 更多信息 ，请 参见此处。 我们从该模型获得的准确性为66％，比我们的基准46％有了显着提高。

As I said earlier, I have already done some feature selection to improve two factors: general accuracy, and the accuracy of the minority classes. This is a list of the current features and their importance in the model. Now let’s think back to our definition of the F-scale — it is simply a measurement of a tornado’s damage to man-made structures and crops. So how does the model perform when it is only fed those columns?

如前所述，我已经进行了一些特征选择以提高两个因素：总体准确性和少数类的准确性。 这是当前功能及其在模型中的重要性的列表。 现在让我们回想一下F刻度的定义-它只是龙卷风对人造结构和农作物造成的损害的一种度量。 那么，当模型仅填充这些列时，其性能如何？

Here is the confusion matrix when the model is trained on only the Fatalities, Injuries, Loss, and Crop Loss columns. The model had a 62 percent accuracy on these four columns. While this may be a surprisingly small drop in accuracy, it is understandable considering these are the most important features to the Fujita Scale.

当仅在“死亡率”，“伤害”，“损失”和“作物损失”列上训练模型时，这是混淆矩阵。 该模型在这四个色谱柱上的准确度为62％。 尽管这可能是精度上出乎意料的小幅下降，但考虑到这些是Fujita秤最重要的功能，这是可以理解的。

So what else can be done to improve the accuracy of the predictions? As we can see in the distributions image, our classes are extremely imbalanced. To counter this natural disparity in the data, there are a couple possible solutions.

那么还有什么可以做来提高预测的准确性呢？ 从分布图中可以看到，我们的类非常不平衡。 为了应对数据中的这种自然差异，有两种可能的解决方案。

The first option is to use the SMOTE algorithm to over-sample the minority classes. The algorithm does this by creating new entries that lie in between the existing entries. While this method doesn’t automatically guarantee that the new entries are realistic, the nearest neighbors methodology should produce realistic entries in my case.

第一种选择是使用SMOTE算法对少数类进行过度采样。 该算法通过创建位于现有条目之间的新条目来实现此目的。 尽管此方法不能自动保证新输入的内容是真实的，但在我的情况下，最近邻居方法应生成实际的输入。

Here is the confusion matrix and the classification report for the SMOTE algorithm. I have added back in all of the columns listed in the feature importance image.

这是SMOTE算法的混淆矩阵和分类报告。 我重新添加了功能重要性图像中列出的所有列。

While the 62 percent accuracy is identical to our previous model, the confusion matrix clearly shows an improvement in correct predictions.

尽管62％的准确度与我们之前的模型相同，但是混淆矩阵清楚地表明了正确预测的改进。

The second option would be utilizing the NearMiss algorithm. This method under-samples the dataset to include less counts of the majority classes.

第二种选择是利用NearMiss算法。 此方法对数据集进行欠采样以包含较少的多数类计数。

The confusion matrix plot looks nearly identical to the SMOTE plot, with slightly tighter spreads around correct predictions and a minor improvement predicting F5’s. As shown by the accuracy and recall scores, the NearMiss model is a small improvement over the SMOTE algorithm.

混淆矩阵图看起来几乎与SMOTE图相同，围绕正确预测的散布略紧，而预测F5的改进较小。 如准确性和召回力得分所示，NearMiss模型是对SMOTE算法的小改进。

I’ve learned two major things during the course of this project. The first thing being that tornadoes are difficult to classify. While 63 percent accuracy certainly isn’t terrible, I know that better scores are achievable (though outside of the scope of this project).

在这个项目的过程中，我学到了两件事。 首先是龙卷风很难分类。 虽然63％的准确度当然并不可怕，但我知道可以取得更好的成绩(尽管超出了该项目的范围)。

The second thing was the knowledge I gained from working with data that contained such divided classes. Before working on this project, I didn’t know about the different techniques used to deal with minority classes. The results I have achieved implementing these techniques should speak for themselves in proving their value.

第二件事是我从处理包含此类分类的数据中获得的知识。 在从事该项目之前，我不了解用于处理少数群体的不同技术。 我实现这些技术所取得的成果应该证明自己的价值。