机器学习属性_属性选择| 机器学习

我就是我 2023-03-05 12:38 11阅读 0赞

机器学习 属性

**All machine learning models are based on two types of values:**

**所有机器学习模型都基于两种类型的值：**

1.  Feature columns
    
    功能栏
2.  Target values contained in a separate column
    
    包含在单独列中的目标值

Let us understand what do these two categories of values mean...

让我们了解这两类价值是什么意思...

**Feature columns:**

**功能栏：**

Feature columns are the columns which are independent entities or in other words, we can say that these values help in predicting the target values. While making the prediction the model generally uses these set of feature values present in feature columns to predict the required target value. There can be multiple feature columns for a single target value however it is not always necessary that all the columns apart from the one containing the target values would be used as feature values.

特征列是作为独立实体的列，也就是说，可以说这些值有助于预测目标值。 在进行预测时，模型通常使用特征列中存在的这些特征值集来预测所需的目标值。 一个目标值可以有多个要素列，但是并非总是必须将除包含目标值的列之外的所有列都用作要素值。

**Target values:**

**目标值：**

This is the dependent values, in other words, we can say that these are the values that are being predicted by our machine learning model. While training our model the data set we use contains the feature columns along with the target values, however when it comes to testing our model the data set used will generally not contain the predefined target values but at times in order to cross-check the prediction made even the test datasets contain the target values.

这是相关值，换句话说，我们可以说这些是机器学习模型正在预测的值。 在训练模型时，我们使用的数据集包含功能列以及目标值，但是在测试模型时，所使用的数据集通常将不包含预定义的目标值，但有时会交叉检查预测使测试数据集也包含目标值。

Let us understand this with the help of an example:

让我们借助示例了解这一点：

**Consider this data set:** **file name: "headbrain.arff"**

**考虑以下数据集：** **文件名：“ headbrain.arff”**

@relation headbrain
    
    @attribute Gender numeric
    @attribute 'Age Range' numeric
    @attribute 'Head Size(cm^3)' numeric
    @attribute 'Brain Weight(grams)' numeric
    
    @data
    1,1,4512,1530
    1,1,3738,1297
    1,1,4261,1335
    1,1,3777,1282
    1,1,4177,1590
    1,1,3585,1300
    1,1,3785,1400
    1,1,3559,1255
    1,1,3613,1355
    1,1,3982,1375
    1,1,3443,1340
    1,1,3993,1380
    1,1,3640,1355
    1,1,4208,1522
    1,1,3832,1208
    1,1,3876,1405
    1,1,3497,1358
    1,1,3466,1292
    1,1,3095,1340
    1,1,4424,1400
    1,1,3878,1357
    1,1,4046,1287
    1,1,3804,1275
    1,1,3710,1270
    1,1,4747,1635
    1,1,4423,1505
    1,1,4036,1490
    1,1,4022,1485
    1,1,3454,1310
    1,1,4175,1420
    1,1,3787,1318
    1,1,3796,1432
    1,1,4103,1364
    1,1,4161,1405
    1,1,4158,1432
    1,1,3814,1207
    1,1,3527,1375
    1,1,3748,1350
    1,1,3334,1236
    1,1,3492,1250
    1,1,3962,1350
    1,1,3505,1320
    1,1,4315,1525
    1,1,3804,1570
    1,1,3863,1340
    1,1,4034,1422
    1,1,4308,1506
    1,1,3165,1215
    1,1,3641,1311
    1,1,3644,1300
    1,1,3891,1224
    1,1,3793,1350
    1,1,4270,1335
    1,1,4063,1390
    1,1,4012,1400
    1,1,3458,1225
    1,1,3890,1310
    1,2,4166,1560
    1,2,3935,1330
    1,2,3669,1222
    1,2,3866,1415
    1,2,3393,1175
    1,2,4442,1330
    1,2,4253,1485
    1,2,3727,1470
    1,2,3329,1135
    1,2,3415,1310
    1,2,3372,1154
    1,2,4430,1510
    1,2,4381,1415
    1,2,4008,1468
    1,2,3858,1390
    1,2,4121,1380
    1,2,4057,1432
    1,2,3824,1240
    1,2,3394,1195
    1,2,3558,1225
    1,2,3362,1188
    1,2,3930,1252
    1,2,3835,1315
    1,2,3830,1245
    1,2,3856,1430
    1,2,3249,1279
    1,2,3577,1245
    1,2,3933,1309
    1,2,3850,1412
    1,2,3309,1120
    1,2,3406,1220
    1,2,3506,1280
    1,2,3907,1440
    1,2,4160,1370
    1,2,3318,1192
    1,2,3662,1230
    1,2,3899,1346
    1,2,3700,1290
    1,2,3779,1165
    1,2,3473,1240
    1,2,3490,1132
    1,2,3654,1242
    1,2,3478,1270
    1,2,3495,1218
    1,2,3834,1430
    1,2,3876,1588
    1,2,3661,1320
    1,2,3618,1290
    1,2,3648,1260
    1,2,4032,1425
    1,2,3399,1226
    1,2,3916,1360
    1,2,4430,1620
    1,2,3695,1310
    1,2,3524,1250
    1,2,3571,1295
    1,2,3594,1290
    1,2,3383,1290
    1,2,3499,1275
    1,2,3589,1250
    1,2,3900,1270
    1,2,4114,1362
    1,2,3937,1300
    1,2,3399,1173
    1,2,4200,1256
    1,2,4488,1440
    1,2,3614,1180
    1,2,4051,1306
    1,2,3782,1350
    1,2,3391,1125
    1,2,3124,1165
    1,2,4053,1312
    1,2,3582,1300
    1,2,3666,1270
    1,2,3532,1335
    1,2,4046,1450
    1,2,3667,1310
    2,1,2857,1027
    2,1,3436,1235
    2,1,3791,1260
    2,1,3302,1165
    2,1,3104,1080
    2,1,3171,1127
    2,1,3572,1270
    2,1,3530,1252
    2,1,3175,1200
    2,1,3438,1290
    2,1,3903,1334
    2,1,3899,1380
    2,1,3401,1140
    2,1,3267,1243
    2,1,3451,1340
    2,1,3090,1168
    2,1,3413,1322
    2,1,3323,1249
    2,1,3680,1321
    2,1,3439,1192
    2,1,3853,1373
    2,1,3156,1170
    2,1,3279,1265
    2,1,3707,1235
    2,1,4006,1302
    2,1,3269,1241
    2,1,3071,1078
    2,1,3779,1520
    2,1,3548,1460
    2,1,3292,1075
    2,1,3497,1280
    2,1,3082,1180
    2,1,3248,1250
    2,1,3358,1190
    2,1,3803,1374
    2,1,3566,1306
    2,1,3145,1202
    2,1,3503,1240
    2,1,3571,1316
    2,1,3724,1280
    2,1,3615,1350
    2,1,3203,1180
    2,1,3609,1210
    2,1,3561,1127
    2,1,3979,1324
    2,1,3533,1210
    2,1,3689,1290
    2,1,3158,1100
    2,1,4005,1280
    2,1,3181,1175
    2,1,3479,1160
    2,1,3642,1205
    2,1,3632,1163
    2,2,3069,1022
    2,2,3394,1243
    2,2,3703,1350
    2,2,3165,1237
    2,2,3354,1204
    2,2,3000,1090
    2,2,3687,1355
    2,2,3556,1250
    2,2,2773,1076
    2,2,3058,1120
    2,2,3344,1220
    2,2,3493,1240
    2,2,3297,1220
    2,2,3360,1095
    2,2,3228,1235
    2,2,3277,1105
    2,2,3851,1405
    2,2,3067,1150
    2,2,3692,1305
    2,2,3402,1220
    2,2,3995,1296
    2,2,3318,1175
    2,2,2720,955
    2,2,2937,1070
    2,2,3580,1320
    2,2,2939,1060
    2,2,2989,1130
    2,2,3586,1250
    2,2,3156,1225
    2,2,3246,1180
    2,2,3170,1178
    2,2,3268,1142
    2,2,3389,1130
    2,2,3381,1185
    2,2,2864,1012
    2,2,3740,1280
    2,2,3479,1103
    2,2,3647,1408
    2,2,3716,1300
    2,2,3284,1246
    2,2,4204,1380
    2,2,3735,1350
    2,2,3218,1060
    2,2,3685,1350
    2,2,3704,1220
    2,2,3214,1110
    2,2,3394,1215
    2,2,3233,1104
    2,2,3352,1170
    2,2,3391,1120

![Sample data set 1][]

Suppose here we want to predict brain weight of a person but on analyzing the data we get to know that there is no relation between the age, gender and the brain weight of a person so in this case age and gender cannot be used as the feature columns so now the question arises that how do we remove these attributes?

假设在这里我们要预测一个人的大脑重量，但是通过分析数据，我们知道年龄，性别和一个人的大脑重量之间没有关系，因此在这种情况下，年龄和性别不能用作特征列，所以现在出现了一个问题，即如何删除这些属性？

One solution is that we remove these attributes and create a new .arff file where we will only have the column having the target value i.e. the brain weight and one attribute column i.e. the Head size column.

一种解决方案是，我们删除这些属性并创建一个新的.arff文件，在该文件中，我们仅具有目标值的列(即大脑权重)和一个具有属性值的列(即“头部大小”列)。

To remove the non-essential attributes we would be using the remove method from the class **weka.filters.unsupervised.attribute.Remove**

要删除非必要属性，我们将使用类**weka.filters.unsupervised.attribute.Remove中**的remove方法。

Let us now write the java code for the following in the Eclipse IDE:

现在，让我们在Eclipse IDE中为以下代码编写Java代码：

import weka.core.Instances;
    import weka.core.converters.ArffSaver;
    import java.io.File;
    import weka.core.converters.ConverterUtils.DataSource;
    import weka.filters.Filter;
    import weka.filters.unsupervised.attribute.Remove;
    
    public class attrib{
        
    	public static void main(String args[]) throws Exception{
        
    		//load the dataset
    		DataSource source = new DataSource("headbrain.arff");
    		//this stores the values of the data in the dataset instances 
    		Instances dataset = source.getDataSet();
    		
    			
    		String[] opts = new String[]{
         "-R", "1"};
    		//create a Remove object to remove the non required attributes
    		Remove remove = new Remove();
    		//set the filter options
    		remove.setOptions(opts);
    		//pass the dataset to the filter
    		remove.setInputFormat(dataset);
    		//apply the filter to remove the first column which is the gender
    		Instances newData = Filter.useFilter(dataset, remove);
    		//applying the same filter again will remove the second column which is the age range
    		remove.setInputFormat(newData);
    		Instances newData1 = Filter.useFilter(newData, remove);
    		
    
    		
    		ArffSaver saver = new ArffSaver();
    		saver.setInstances(newData1);
    		saver.setFile(new File("C:\\Users\\Logan\\Desktop\\ML\\headbraina.arff"));
    		saver.writeBatch();
    		System.out.println("The final dataset after removing non essential attributes is as follows");
    		System.out.println(newData1);
    	}
    }

**Output**

**输出量**

![attribute selection example output][]

In this way, we can successfully remove the non-essential attributes using java programming in order to make our data look less complex, which will help in the easy analysis of the data set. This was all for today guys hope you liked it. Have a great day ahead.

这样，我们可以使用Java编程成功删除非必要属性，以使我们的数据看起来不太复杂，这将有助于轻松分析数据集。 今天就这些，所有人都希望您喜欢它。 祝您有美好的一天。

> 翻译自: [https://www.includehelp.com/ml-ai/attribute-selection.aspx][https_www.includehelp.com_ml-ai_attribute-selection.aspx]

机器学习 属性

[Sample data set 1]: /images/20230208/d4912a343d5145a78e49abf88135e75a.png
[attribute selection example output]: /images/20230208/3816ab3a471041a4ab3102214f18957f.png
[https_www.includehelp.com_ml-ai_attribute-selection.aspx]: https://www.includehelp.com/ml-ai/attribute-selection.aspx