Samplings in machine learning

A large amount of data has been produced every day by different digital systems. This unprecedented data quantity causes difficulties with its collection and further processing by machine learning algorithms.

The computing possibilities of machines are limited. That is why it is essential to collect information and select a particular data subset from it. Otherwise, the future machine learning model may result in inaccurate and inadequate outputs.

There are a variety of data collection techniques. One of the most popular tools is data sampling. This statistical method allows gathering a data subset from an infinitely many.

Sampling: what is it?

To understand the basic principle of the sampling method, let’s look at the following example. Imagine that you have to give an opinion concerning the sweetness of the juice. Of course, it is not necessary to drink a whole bottle of juice. It is enough to take a sip and to decide whether it is sweet or not. In this case, a small gulp is a part of the population. It is a simple example of sampling. But how can we use this method in more complicated research?

For instance, we need to define the average age of people living in the countryside. Indeed, it is impossible to take into account every villager and determine their age. It is more rational to take samples and discover the patterns from studying them.

Samplings in data analysis are effective and beneficial. Correctly executed by ML experts, it significantly saves time, sparing us from analyzing the whole population.

Three simple steps in sampling

The process of sampling contains several steps.

The first step is to identify the target population you are going to analyze. The target variety is a set of objects from which we take sampling. A precise definition of the target set and frame reduces the probability of data inclusion, which is not appropriate for the study. For example, we intend to define the number of luxury electric cars in the city. There is no need to analyze poor districts. Targets will be a city center and wealthy regions.

The second step is to determine which sampling method to use: probability or non-probability. If the target population is equal to the sampling frame, it is possible to use a random selection method. On the contrary, if the sampling frame does not consist of the same objects as the target set, it is better to apply a non-random method.

The last step is a definition of sampling size and data collection. By this step, we decide how many parts of the subset to take into consideration. The bigger the sample size, the more resources we will need. After size differentiation, it is time to choose samples.

Sampling approaches

All sampling approaches can be divided into 2 groups. Depending on the goals and scope, we may use a probability (or random) technique or a non-probability method.

Probability approaches

These groups of approaches imply that every sample from a population can be chosen. Each part of the dataset represents the whole. There are 4 main types of probability samplings.

An ordinary random method is the simplest one as it allows selecting any part of the population independently from its characteristics. This technique significantly saves your resources. It is possible to use random number generators methods. For example, you want to hold a contest between all your subscribers on Instagram. Applying a simple random approach means that you give all subscribers particular numbers. Then using the generator, you select numbers, which will get a prize from you.

Systematic sampling is similar to a random method, but the difference is that we select objects at certain intervals. Concerning our example above, we may hold an Instagram contest only among every tenth subscriber.

According to stratified sampling, we divide the entire population into several subgroups. Each subgroup has a fixed set of characteristics (age, nationality, occupation, and so on). The idea of this approach is to pick up samplings from each group and collect a data-subset.

The clustering method is effective when we study a large amount of data. The basic principle of this approach is the same as a stratified method. You divide a population into subsets, but these subsets must have features common to the entire population. Cluster sampling is the riskiest as a large amount of data increases the likelihood of errors.

Non-Probability approaches

If probability approaches are used primarily in quantitative analyses, non-probability approaches are useful for qualitative research. This method is not based on random selection.

Convenience sampling involves objects, which are more accessible for study than others are. Such a method is accessible and simple, but the results may be inaccurate as samples may not reflect the distinctive feature of the whole. As an example, you plan to do a survey in the company about job satisfaction among women. However, the results can not be accurate as not all female workers are at work now. Some of them are on vacations, some on maternity leave, and so on.

A selective sampling supposes that an expert chooses samples according to his own opinion. If he thinks these samples are the most appropriate for the study, it is better to select them ignoring others. Such an approach is also called a judgment method. It is a subject to a human factor.

A snowball sampling is advantageous when it is hard to define the actual sample frame. For instance, if we intend to hold an opinion poll, we may select one person and ask him to recommend further objects for surveying. The next group of respondents, in turn, give us other candidates for questioning. Thus a snowball forms.

Conclusion

It is up to you what kind of approaches in sampling to use. Nevertheless, it is vital to remember that a proper sample must have specific features.

Firstly, it should have adequate size. If your sample is too large, you may have difficulties with analyzing the data. If you need help from professional developers, you can always ask for free advice here.

Secondly, the picked sample has to represent the main features of the population. Otherwise, we may receive the results having nothing to do with our study.

Lastly, the picked out sample must be accurate. It is important to determine possible deviations in samples.

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