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  • 1.打乱数据 shuffle 我们有下面以个DataFrame 我们可以看到BuyInter的数值是按照0,-1,-1,2,2,2,3,3,...sklearn(机器学习的库)中也有shuffle的方法 from  sklearn.utils  import  shuffle    df = shuffle(df) 

    1.打乱数据 shuffle

    我们有下面以个DataFrame



    我们可以看到BuyInter的数值是按照0,-1,-1,2,2,2,3,3,3,3这样排列的,我们希望不保持这个次序,但是同时列属性又不能改变。

    方法1:sample

     df.sample(frac=1这样对可以对df进行shuffle。其中参数frac是要返回的比例,比如df中有10行数据,我只想返回其中的30%,那么frac=0.3。

    补充:

    DataFrame.sample(n=Nonefrac=Nonereplace=Falseweights=Nonerandom_state=Noneaxis=None)

    random_state:随机种子,固定值,控制每次随机抽样产生的结果相同


    方法2:sklearn(机器学习的库)中也有shuffle的方法

    1. from sklearn.utils import shuffle  
    2. df = shuffle(df) 

    展开全文
  • 机器学习训练集测试集Prerequisite: 先决条件: Introduction to weka and Machine learning in Java Java Weka和机器学习简介 Attribute relation file format | Machine Learning 属性关系文件格式| 机器学习 ...

    机器学习训练集和测试集

    Prerequisite:

    先决条件:

    Well, those who haven’t yet read my previous articles should note that for machine learning in java I am using a weka.jar file to import the required machine learning classes into my eclipse IDE. I will suggest you guys have a look at my article on data splitting using Python programming language.

    好吧,那些尚未阅读我以前的文章的人应该注意,对于Java中的机器学习,我正在使用weka.jar文件将所需的机器学习类导入到我的Eclipse IDE中。 我建议你们看看我有关使用Python编程语言进行数据拆分的文章。

    Let’s have a look at the basic definition of training and test sets before we proceed further.

    在继续进行之前,让我们看一下训练和测试集的基本定义。

    训练套 (Training Set)

    The purpose of using the training set is as the name suggests is to train our model by feeding in the attributes and the corresponding target value into using the values in the training our model can identify a pattern which will be used by our model to predict the test set values.

    顾名思义,使用训练集的目的是通过输入属性和相应的目标值以训练模型中的值来训练我们的模型,我们的模型可以识别出一种模式,我们的模型将使用该模式来预测测试设定值。

    测试集 (Test Set)

    This set is used to check the accuracy of our model and as the name suggest we use this dataset to perform the testing of our result. This data set usually contains the independent attributes using which our model predicts the dependent value or the target value. Using the predicted target values we further compare those values with the predefined set of the target values in our test set in order to determine the various evaluating parameters like RMSE,percentage accuracy, percentage error, area under the curve to determine the efficiency of our model in predicting the dependent values which in turn determines the usefulness of our model.

    该集合用于检查模型的准确性,顾名思义,我们使用该数据集对结果进行测试。 该数据集通常包含独立属性,我们的模型将使用这些独立属性来预测相关值或目标值。 使用预测的目标值,我们进一步将这些值与测试集中的目标值的预定义集进行比较,以确定各种评估参数,例如RMSE,百分比精度,百分比误差,曲线下面积,以确定模型的效率预测相关值,进而确定模型的实用性。

    For detailed information about training and test set, you can refer to my article about data splitting.

    有关培训和测试集的详细信息,您可以参考我有关数据拆分的文章。

    Another important feature that we are going to talk about is the cross-validation. Well, in order to increase the accuracy of our model we use cross-validation. Suppose if we split our data in such a way that we have 100 set of values and we split first 20 as testing sets and rest as the training sets, well since we need more data for training the splitting ratio we used here is completely fine but then there arise many uncertainties like what if the first 20 sets of data have completely opposite values from the rest of data one way to sort this issue is to use a random function which will randomly select the testing and training set values so now we have reduced chances of getting biased set of values into our training and test sets but still we have not fully sorted the problem there are still chances that maybe the randomized testing data set has the values which aren’t at all related to the training set values or it might be that the values in the test set are exactly the same as that of training set which will result in overfitting of our model ,you can refer to this article if you want to know more about overfitting and underfitting of the data.

    我们将要讨论的另一个重要功能是交叉验证。 好吧,为了提高我们模型的准确性,我们使用了交叉验证。 假设如果我们以100个值集的方式拆分数据,然后将前20个值拆分为测试集,其余的拆分为训练集,那么由于我们需要更多的数据来训练拆分率,因此这里使用的方法完全可以,但是那么就会出现许多不确定性,例如如果前20组数据与其余数据具有完全相反的值,该问题排序的一种方法是使用随机函数,该函数将随机选择测试和训练集的值,因此现在我们减少了可能会在我们的训练和测试集中引入偏向的值集,但仍然没有完全解决问题,仍然有可能随机化的测试数据集具有与训练集值完全不相关的值,或者可能是测试集中的值与训练集中的值完全相同,这将导致我们的模型过度拟合,如果您想了解更多关于t的过度拟合和不足的信息 ,可以参考本文 他数据

    Well, then how do we solve this issue? One way is to split the data n times into training and testing sets and then find the average of those splitting datasets to create the best possible set for training and testing. But everything comes with a cost since we are repeatedly splitting out data into training and testing the process of cross-validation consumes some time. But then it is worth waiting if we can get a more accurate result.

    好吧,那我们怎么解决这个问题呢? 一种方法是将数据n次分割为训练和测试集,然后找到这些分割数据集的平均值,以创建最佳的训练和测试集。 但是,一切都是有代价的,因为我们要反复将数据分成训练和测试交叉验证的过程,这会花费一些时间。 但是,如果我们可以获得更准确的结果,那就值得等待。

    Training and Testing Sets in Java | Machine Learning

    Image source: https://upload.wikimedia.org/wikipedia/commons/1/1c/K-fold_cross_validation_EN.jpg

    图片来源: https : //upload.wikimedia.org/wikipedia/commons/1/1c/K-fold_cross_validation_EN.jpg

    While writing the code I would be using a variable named as fold or K as shown in the above figure which signifies the no of times to perform the cross-validation.

    在编写代码时,我将使用一个名为fold或K的变量,如上图所示,它表示没有时间执行交叉验证。

    Below is the java code is written for generating testing and training sets in the ratio of 1:4(approx.) which is an optimal ratio of splitting the data sets.

    下面是编写Java代码以生成测试和训练集的比例为1:4(大约)的比率,这是拆分数据集的最佳比率。

    The data set I have used can be copied from here: File name: "headbraina.arff"

    我使用的数据集可以从这里复制: 文件名:“ headbraina.arff”

    @relation headbrain-weka.filters.unsupervised.attribute.Remove-R1-weka.filters.unsupervised.attribute.Remove-R1
    
    @attribute 'Head Size(cm^3)' numeric
    @attribute 'Brain Weight(grams)' numeric
    
    @data
    4512,1530
    3738,1297
    4261,1335
    3777,1282
    4177,1590
    3585,1300
    3785,1400
    3559,1255
    3613,1355
    3982,1375
    3443,1340
    3993,1380
    3640,1355
    4208,1522
    3832,1208
    3876,1405
    3497,1358
    3466,1292
    3095,1340
    4424,1400
    3878,1357
    4046,1287
    3804,1275
    3710,1270
    4747,1635
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    4175,1420
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    3796,1432
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    3527,1375
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    3492,1250
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    3505,1320
    4315,1525
    3804,1570
    3863,1340
    4034,1422
    4308,1506
    3165,1215
    3641,1311
    3644,1300
    3891,1224
    3793,1350
    4270,1335
    4063,1390
    4012,1400
    3458,1225
    3890,1310
    4166,1560
    3935,1330
    3669,1222
    3866,1415
    3393,1175
    4442,1330
    4253,1485
    3727,1470
    3329,1135
    3415,1310
    3372,1154
    4430,1510
    4381,1415
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    3858,1390
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    3824,1240
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    3876,1588
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    3618,1290
    3648,1260
    4032,1425
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    3916,1360
    4430,1620
    3695,1310
    3524,1250
    3571,1295
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    3900,1270
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    3937,1300
    3399,1173
    4200,1256
    4488,1440
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    3391,1125
    3124,1165
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    3666,1270
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    3779,1520
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    3803,1374
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    3158,1100
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    3703,1350
    3165,1237
    3354,1204
    3000,1090
    3687,1355
    3556,1250
    2773,1076
    3058,1120
    3344,1220
    3493,1240
    3297,1220
    3360,1095
    3228,1235
    3277,1105
    3851,1405
    3067,1150
    3692,1305
    3402,1220
    3995,1296
    3318,1175
    2720,955
    2937,1070
    3580,1320
    2939,1060
    2989,1130
    3586,1250
    3156,1225
    3246,1180
    3170,1178
    3268,1142
    3389,1130
    3381,1185
    2864,1012
    3740,1280
    3479,1103
    3647,1408
    3716,1300
    3284,1246
    4204,1380
    3735,1350
    3218,1060
    3685,1350
    3704,1220
    3214,1110
    3394,1215
    3233,1104
    3352,1170
    3391,1120
    
    
    

    Code:

    码:

    import weka.core.Instances;
    
    import java.io.File;
    import java.util.Random;
    
    import weka.core.converters.ArffSaver;
    import weka.core.converters.ConverterUtils.DataSource;
    import weka.classifiers.Evaluation;
    import weka.classifiers.bayes.NaiveBayes;
    
    public class testtrainjaava{
    	public static void main(String args[]) throws Exception{
    		//load dataset
    		DataSource source = new DataSource("headbraina.arff");
    		Instances dataset = source.getDataSet();	
    		//set class index to the last attribute
    		dataset.setClassIndex(dataset.numAttributes()-1);
    
    		int seed = 1;
    		int folds = 15;
    		
    		//randomize data
    		Random rand = new Random(seed);
    		
    		//create random dataset
    		Instances randData = new Instances(dataset);
    		randData.randomize(rand);
    		
    		//stratify	    
    		if (randData.classAttribute().isNominal())
    			randData.stratify(folds);
    
    		// perform cross-validation	    	    
    		for (int n = 0; n < folds; n++) {
    			//Evaluation eval = new Evaluation(randData);
    			//get the folds	      
    			Instances train = randData.trainCV(folds, n);
    			Instances test = randData.testCV(folds, n);	      
    
    			ArffSaver saver = new ArffSaver();
    			saver.setInstances(train);
    			System.out.println("No of folds done = " + (n+1));
    
    			saver.setFile(new File("trainheadbraina.arff"));
    			saver.writeBatch();
    			//if(n==9)
    			//{System.out.println("Training set generated after the final fold is");
    			//System.out.println(train);}
    
    			ArffSaver saver1 = new ArffSaver();
    			saver1.setInstances(test);
    			saver1.setFile(new File("testheadbraina1.arff"));
    			saver1.writeBatch();
    		}
    	}
    }
    
    

    Output

    输出量

    Training and Testing Sets in Java Output 1

    After getting this output just go to the destination folder in which you have to save the training and testing data sets and you should see the following results.

    获得此输出后,只需转到目标文件夹,您必须在其中保存训练和测试数据集,并且应该看到以下结果。

    Dataset generated for training the model

    生成用于训练模型的数据集

    Training and Testing Sets in Java Output 2

    Dataset generated for testing the model

    生成用于测试模型的数据集

    Training and Testing Sets in Java Output 3

    This was all for today guys hope you liked this, feel free to ask your queries and have a great day ahead.

    今天,这就是所有这些家伙希望您喜欢的东西,随时询问您的问题,并祝您有美好的一天。

    翻译自: https://www.includehelp.com/ml-ai/training-and-testing-sets-in-java.aspx

    机器学习训练集和测试集

    展开全文
  • 一般情况下,机器学习需要划分为训练集测试集两个部分,训练集用来训练算法,测试集用来测泛化误差等。一般情况下,训练集所占的比重应该在2/3到4/5。如果训练集太大,那么根据测试集的评估结果不够准确,如果测试...

    一般情况下,机器学习需要划分为训练集和测试集两个部分,训练集用来训练算法,测试集用来测泛化误差等。一般情况下,训练集所占的比重应该在2/3到4/5。如果训练集太大,那么根据测试集的评估结果不够准确,如果测试集过大,那么训练集与总体样本差别太大,不一定能反应总体的特征。

    基于scikit-learn,可以简单的做数据分集。

    首先安装sklearn需要提前安装numpy和scipy两个库

    安装命令:

    pip install numpy

    pip install scipy

    pip install -U scikit-learn

    随机抽样

    打开python,(最近我发现jupyter 比pycharm在数据分析上好用很多。。。)

    from sklearn.model_selection import train_test_split

    train_set, test_set = train_test_split( X, y, test_size, random_state)

    举例说明

    from sklearn.model_selection import train_test_split

    import numpy as np

    X = np.arange(10).reshape((-1, 2))

    y =lsit(range(5))

    我们看下X, 和y

    55adfd59acba14c159d5505d7abc14c3.png

    ab62a5ae4eeb7b607b327a7fa592d2ae.png

    X_train, X_test, y_train, y_test = train_test_split(X, y, 

    test_size = 0.2, random_state = 42) 

    test_size参数是测试集占的比率,random_state是随机数种子

    看下结果

    2348ee4133b54601db2fdc6dc2b6ffe0.png

    这样我们就完成了随机抽样,把X和y分成了训练集和测试集,一般情况下,y是X的标记。

    展开全文
  • 机器学习 训练验证测试If we think about what a Machine Learning model does, we can see how its main job is that of finding those rules governing the relationship between input and output. Once found ...

    机器学习 训练验证测试

    If we think about what a Machine Learning model does, we can see how its main job is that of finding those rules governing the relationship between input and output. Once found those rules, the idea is that of applying them to new data and make predictions about their related output.

    如果我们考虑一下机器学习模型的功能,我们可以看到它的主要工作是找到那些控制输入和输出之间关系的规则。 一旦找到这些规则,便是将其应用于新数据并对其相关输出进行预测的想法。

    Henceforth, being predictions the final goal of an ML algorithm, it is pivotal for it to be properly generalized and not too adapted on data it trained on.

    今后,作为预测ML算法的最终目标,对其进行适当的概括而不是过于适应其训练的数据至关重要。

    In this article, we are going to examine different options you have whenever training an ML model.

    在本文中,我们将研究训练ML模型时可以使用的不同选项。

    在整个数据集上训练和评估模型 (Train and evaluate the model on the whole dataset)

    Image for post

    Needless to say, this first approach will lead to a biased result. If we evaluate the model in the very same dataset it trained on, we will probably face the curse of overfitting, which happens whenever the model is too adapted to training data. Regarding the evaluation phase, we will probably get a very high score, yet it is not the score we are looking at: it probably derived from the fact that the algorithm learnt the patterns of that specific dataset and their associated output. Hence, while returning the correct output, it is not exploiting a general rule, it is just reproducing the observed patterns.

    不用说,这第一种方法将导致有偏见的结果。 如果我们在与训练数据集相同的数据集中评估模型,那么我们可能会面临过度拟合的诅咒,每当模型过于适应训练数据时就会发生这种情况。 关于评估阶段,我们可能会获得很高的分数,但这并不是我们正在关注的分数:它可能源自以下事实:该算法学习了该特定数据集的模式及其关联的输出。 因此,在返回正确的输出时,它并没有利用一般规则,而只是再现观察到的模式。

    How it is supposed to work on new, never-seen-before data? Of course, it will not be reliable. We need to improve our training and evaluation phases.

    它应该如何处理从未见过的新数据? 当然,这将是不可靠的。 我们需要改善培训和评估阶段。

    将数据分为训练和测试集 (Splitting data into training and test set)

    Image for post

    With this approach, we are keeping apart one portion of the dataset and training the model on the remaining portion. By doing so, we are left with a small set of data, called test set, the model has never seen before, hence it is a more reliable benchmark for evaluation purposes. Indeed, if we evaluate the model on the test set and obtain a great score, we are more confident to say that this model is well generalized.

    通过这种方法,我们将数据集的一部分分开,并在其余部分上训练模型。 这样一来,我们剩下的一小部分数据称为测试集,该模型从未见过,因此它是用于评估目的的更可靠基准。 的确,如果我们在测试集上评估该模型并获得高分,则我们更有信心地说该模型已被很好地推广。

    However, there is still one caveat in this method. There is an infinite number of possible combinations of train-test sets, however, we only experimented one. How do we know that the very splitting we obtained is the most representative one? Maybe, if we had a different composition of train and test set, we would have a very different result.

    但是,此方法仍然有一个警告 。 火车测试集的可能组合有无数种,但是,我们仅进行了试验。 我们怎么知道我们获得的最分裂是最有代表性的分裂? 也许,如果我们在训练和测试集的构成上有所不同,那么结果将大不相同。

    To bypass this problem, we can introduce the concept of cross-validation.

    为了绕过这个问题,我们可以引入交叉验证的概念。

    交叉验证 (Cross-validation)

    The idea of cross-validation arises because of the caveat explained above. It basically wants to guarantee that the score of our model does not depend on the way we picked the train and test set.

    交叉验证的想法的产生是因为的警告如上所述。 它基本上是要保证我们模型的分数不取决于我们选择火车和测试集的方式。

    It works as follows. It splits our dataset into K-folds, then the model is trained on K-1 folds and tested on the remaining one, for K iterations. So each time, because of the K rotations of the test set, the model is trained and tested on a new composition of data.

    它的工作原理如下。 它将我们的数据集分成K折,然后在K-1折上训练模型,并在其余的K折上进行测试。 因此,每次由于测试集的K旋转,都会在新的数据组合上对模型进行训练和测试。

    Image for post

    Are we now confident that our model will perform well on new data? Is it enough generalized? Well, not really.

    我们现在是否有信心我们的模型将在新数据上表现良好? 是否足够概括? 好吧,不是真的。

    Even though we are getting closer to the “optimal” solution, there is still a drawback to be addressed.

    即使我们越来越接近“最佳”解决方案,仍然存在要解决的缺陷。

    培训,验证和测试集 (Train, validation and test set)

    The caveat of cross-validation as explained above is that we are evaluating the model on a test set which is not completely extraneous from the model itself. Imagine we start a cross-validation procedure: at the first iteration, the model will be evaluated on new data, since this is the very first split of the dataset; at iteration 2, however, the test set will include also some data point that, in the previous iteration, where part of the training set, hence the model has actually seen it before! Basically, we are falling again into the first scenario of this article.

    如上所述,交叉验证的警告是,我们正在测试集上评估模型,该测试集与模型本身并不完全无关。 想象一下,我们开始一个交叉验证过程:在第一次迭代中,将对新数据评估模型,因为这是数据集的第一个拆分; 但是,在迭代2时,测试集还将包含一些数据点,该数据点在上一次迭代中是训练集的一部分,因此模型实际上已经在之前看到过! 基本上,我们将再次陷入本文的第一种情况。

    Luckily, there is an easy way to fix it, and it consists of introducing a third set, the validation set.

    幸运的是,有一种简单的方法可以对其进行修复,其中包括引入第三组验证集。

    Image for post

    Basically, we first split data into train and test set. Then, we keep apart the test set and further split the train set into train and validation sets. By doing so, when applying cross-validation, we first evaluate the model over the K possible combinations. Then, once obtained a validation score, we are ready to try the model on the test set. Now, we are guaranteed that a high score in the test set is an index of a well-generalized model.

    基本上,我们首先将数据分为训练和测试集。 然后,我们将测试集分开,然后将训练集进一步分为训练集和验证集。 这样,在应用交叉验证时,我们首先在K个可能的组合上评估模型。 然后,一旦获得验证分数,我们准备在测试集上尝试模型。 现在,我们可以保证测试集中的高分是一个通用模型的指标。

    结论 (Conclusions)

    Choosing the proper training and validation approach is crucial whenever you build an ML model. However, it is even more important the awareness that every approach has its drawbacks, and to keep them into considerations while drawing conclusions.

    每当构建ML模型时,选择正确的培训和验证方法都是至关重要的。 但是,更重要的是要意识到每种方法都有其缺点,并在得出结论时将其考虑在内。

    In this article, the final approach is probably the most accurate one, yet it leads to further problems, the first one being the reduction of training data, which is another cause of overfitting.

    在本文中,最后一种方法可能是最准确的方法,但它会导致进一步的问题,第一种方法是减少训练数据,这是过拟合的另一个原因。

    So it is pivotal to choose a technique depending on the specific task you are going to solve, keeping in mind all the pros and cons of each method.

    因此,要牢记每种方法的优点和缺点,根据要解决的特定任务选择一种技术至关重要。

    翻译自: https://medium.com/analytics-vidhya/training-validation-and-test-set-in-machine-learning-7fab555c1080

    机器学习 训练验证测试

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